Systems and methods for pervasive advisor for major expenditures

ABSTRACT

A pervasive advisor for major purchases and other expenditures may detect that a customer is contemplating a major purchase (e.g., through active listening). The advisor may assist the customer with the timing and manner of making the purchase in a way that is financially sensible in view of the customer&#39;s financial situation. A customer may be provided with dynamically-updated information in response to recent actions that may affect an approved loan amount and/or interest rate. Underwriting of a loan may be triggered based on the geo-location of the user. Financial advice may be provided to customers to help them meet their goals using information obtained from third party sources, such as purchase options based on particular goals. The pervasive advisor may thus intervene to assist with budgeting, financing, and timing of major expenditures based on the customer&#39;s location and on the customer&#39;s unique and changing circumstances.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/195,613 entitled “SYSTEMS AND METHODS FOR PERVASIVE ADVISOR FOR MAJOREXPENDITURES,” filed Nov. 19, 2018 (now U.S. Pat. No. 10,943,308), whichclaims priority to U.S. Provisional Patent Application No. 62/666,591entitled “SYSTEMS AND METHODS FOR PERVASIVE ADVISOR FOR MAJOREXPENDITURES,” filed May 3, 2018, and to U.S. Provisional PatentApplication No. 62/666,587 entitled “SYSTEMS AND METHODS FOR PROACTIVELISTENING BOT-PLUS PERSON ADVICE CHAINING,” filed May 3, 2018, all ofwhich are incorporated herein by reference in their entireties.

TECHNICAL FIELD

The present disclosure relates to systems, devices, and methods forproviding situationally-aware, pervasive advising for achievingfinancial goals, such as major expenditures and loan underwritingtherefor.

BACKGROUND

People are often unintentional about how they spend money. Withoutnecessarily realizing it, many people tend to spend more than they canafford. The financial health of a person is correlated to the financialdecisions that person makes. When it comes to making a majorexpenditure, most people tend to determine how to obtain funds to payfor the expenditure on their own without the assistance of a financialadvisor. For example, when a person wishes to buy a house or car, he orshe may receive information from a financial institution as to how muchof a loan the person qualifies for and at what interest rate. However,the person may not receive advice as to whether it is the right time forthe person to buy a car or home, or how much to spend on the car or homebased on his or her unique circumstances (even if the person qualifiesfor a larger loan).

Moreover, customers want to be assured that they are purchasing whatthey want, and they often spend a substantial amount of time making adecision about major expenditures (e.g., time spent researching and testdriving cars, searching for available homes and visiting open houses,etc.). Having spent that time on the decision of what to buy, thecustomer may not wish or be able to devote more time and energy on themajor expenditure. Customers thus often spend much more time on decidingon which item to buy (e.g., which car, house, or jewelry) than on howbest to acquire the item from a financial standpoint. For example, aperson may spend time determining which car he or she would like topurchase, then go to a dealership with little to no information on howmuch money to borrow and at what interest rate. While at a locationwhere a person can make a major purchase, the person typically has toobtain funds to finance the purchase after he or she has discussed aparticular item or property. The customer may, for example, go to adealership and rely on the dealer to arrange for financing (e.g., bycontacting financial institutions on behalf of the customer and helpingwith the loan application process). Such systems and models are not onlyreactive, slow, and cumbersome, they also leave such persons susceptibleto making major expenditures without a budget or financial plan, andpotentially spending more than they can afford.

What are needed are systems and methods that address one or more of theabove, as well as other, shortcomings of conventional approaches.

SUMMARY

Example systems and methods relate to providing a pervasive advisorstructured to generate an expense strategy. One or more user computingdevices may detect a voice input indicative of a financial goal (such asa major purchase). An expense strategy structured to meet the financialgoal may be provided in response to the detection of the voice input.The expense strategy may be provided to a customer associated with theuser computing device. In some implementations, underwriting may occurin response to detection of particular locations of computing devices.Underwriting may be based on internal data so that credit scores withcredit agencies need not be impacted.

Various embodiments of the disclosure relate to a service providercomputing system. The service provider computing system may beconfigured to provide a pervasive advisor structured to generate anexpense strategy for an expenditure. The system may comprise a databasewith a user profile corresponding to a user. The system may alsocomprise a network interface configured to communicatively couple theservice provider computing system with a mobile device. The mobiledevice may have a sound sensor. The mobile device may also have one ormore user interfaces. The mobile device may be configured to use thesound sensor to detect ambient sounds. The mobile device may also beconfigured to use the one or more user interfaces to perceptibly presentinformation to the user. At least one of the mobile device and theservice provider computing system may be configured to extract one ormore voice inputs of the user from a subset of the ambient soundsdetected using the sound sensor of the mobile device. At least one ofthe mobile device and the service provider computing system may also beconfigured to identify an expenditure based at least in part on ananalysis of the one or more voice inputs. At least one of the mobiledevice and the service provider computing system may moreover beconfigured to formulate an expense strategy for the expenditure based atleast in part on the expenditure and the user profile. At least one ofthe mobile device and the service provider computing system mayadditionally be configured to present the expense strategy to the uservia the one or more user interfaces of the mobile device.

In one or more implementations, formulating the expense strategy maycomprise generating a loan to be used for the expenditure. Presentingthe expense strategy may comprise presenting the loan as an option forpaying for the expenditure.

In one or more implementations, the voice input may be identified in thesubset of ambient sounds using a biometric voice signature of the user.

In one or more implementations, analysis of the one or more voice inputsmay comprise identifying a plurality of fragmented issue indicators. Theexpenditure may be identified based at least in part on a combination offragmented issue indicators.

In one or more implementations, the plurality of fragmented issueindicators are phrases spoken by the user on different days.

In one or more implementations, at least one of the mobile device andthe service provider computing system may be configured to determine aphysical location of the mobile device. The physical location may bedetermined based on a mobile wallet transaction executed via a mobilewallet application running on the mobile device.

In one or more implementations, the mobile device may further comprise alocation sensor. At least one of the mobile device and the serviceprovider computing system may be configured to determine a physicallocation of the mobile device using the location sensor.

In one or more implementations, at least one of the mobile device andthe service provider computing system may be configured to present theexpense strategy when the physical location is a predetermined physicallocation.

In one or more implementations, at least one of the mobile device andthe service provider computing system may be configured to continue topresent the expense strategy only while the user remains within apredetermined radius of the physical location.

In one or more implementations, at least one of the mobile device andthe service provider computing system may be configured to formulate theexpense strategy based at least in part on the physical location of themobile device.

In one or more implementations, at least one of the mobile device andthe service provider computing system may be configured to identify theexpenditure based at least in part on the physical location of themobile device.

In one or more implementations, at least one of the mobile device andthe service provider computing system is configured to identify theexpenditure based at least in part on proximity of the mobile device toa merchant at the physical location.

In one or more implementations, the mobile device may be a first mobiledevice. At least one of the first mobile device and the service providercomputing system may be communicatively coupled to a second mobiledevice. The second mobile device may comprise a location sensorconfigured to determine a physical location of the second mobile device.At least one of the service provider computing system, the first mobiledevice, and the second mobile device may be configured to identify theexpenditure based at least in part on the physical location of thesecond mobile device determined using the location sensor of the secondmobile device.

In one or more implementations, the first mobile device may be a smartspeaker, and the second mobile device may be a smartphone.

In one or more implementations, the expense strategy includes a loanunderwritten via the service provider computing system. Presenting theexpense strategy may comprise presenting the loan as an option forpaying for the expenditure. At least one of the service providercomputing system and the mobile device may be configured to present theexpense strategy in real-time or near real-time. The expense strategymay be presented such that initially-presented loan data may be updatedbased on subsequent transactions. The subsequent transactions may bedetected by at least one of the mobile device and the service providercomputing system.

Various embodiments of the disclosure relate to a computing device. Thecomputing device may be configured to provide a pervasive advisor. Thecomputing device may comprise a network interface configured tocommunicate with a service provider computing system via atelecommunications network. The computing device may also comprise asound sensor configured to detect ambient sounds. The computing devicemay moreover comprise one or more user interfaces for presenting data.The computing device may additionally comprise a processor and memoryhaving instructions that, when executed by the processor, cause theprocessor to perform specific functions. The computing device may beconfigured to detect one or more sound samples using the sound sensor.The computing device may also be configured to extract one or more voiceinputs of a user from a subset of the one or more sounds samples. Thecomputing device may moreover may configured to identify an expenditurebased at least in part on the one or more voice inputs. The computingdevice may additionally be configured to receive loan data from theservice provider computing system. The loan data may be for a loangenerated for the expenditure. The loan may be generated by the serviceprovider computing system based at least in part on the expenditureand/or a user profile. The computing device may further be configured topresent the loan to the user via the one or more user interfaces.

In one or more implementations, extracting the one or more voice inputsmay comprise identifying one or more phrases spoken by the user. Theexpenditure may be identified based at least in part on the spokenphrases.

In one or more implementations, the subset of sound samples may beidentified based on a biometric voice signature of the user.

In one or more implementations, the computing device may comprise alocation sensor. The computing device may be configured to detect aphysical location of the computing device. The computing may also beconfigured to present the loan when the physical location is apredetermined physical location.

Various embodiments of the disclosure relate to a method. The method maybe for providing a pervasive advisor. The method may comprise detectingambient sounds using a sound sensor of a computing device. The methodmay also comprise extracting one or more voice inputs of a user from asubset of the ambient sounds detected using the sound sensor. The methodmay moreover comprise identifying an expenditure based at least in parton the one or more voice inputs. The method may moreover compriseformulating an expense strategy for the expenditure. The expensestrategy may be formulated based at least in part on the expenditure anda user profile. The expense strategy may include a loan for theexpenditure. The method may additionally comprise presenting the expensestrategy to the user. The expense strategy may be presented via one ormore user interfaces of the computing device.

In one or more implementations, the one or more voice inputs may beidentified using a biometric voice signature of the user.

In one or more implementations, the method may comprise determining aphysical location of the computing device using a location sensor of thecomputing device. The method may also comprise basing identification ofthe expenditure on the physical location. The method may moreovercomprise basing formulation of the expense strategy on the physicallocation. The method may additionally comprise basing presentation ofthe expense strategy on the physical location.

Various embodiments of the disclosure relate to a method. The method maycomprise detecting a physical location of a mobile device of a user. Thephysical location of the mobile device may be detected based at least inpart on an activity of the mobile device. The method may also comprisedetermining that the physical location corresponds with a source ofgoods or services. The method may moreover comprise generating a loanfor the goods or services. The method may additionally comprisepresenting the loan to the user via one or more user interfaces of themobile device as an option for paying for the goods and services.

In one or more implementations, the activity may be a sensor reading ofa location sensor of the mobile device.

In one or more implementations, the activity may be a transactionexecuted via a mobile wallet application running on the mobile device.

In one or more implementations, determining that the physical locationcorresponds with a source of goods or services may comprisecross-referencing the physical location with merchant data in adatabase.

In one or more implementations, the database may be a third-partydatabase accessed via a telecommunications network.

In one or more implementations, the method may comprise determining thegoods or services are goods or services desired or needed by the user.

In one or more implementations, the loan may be not presented to theuser unless the goods or services are determined to be goods or servicesdesired or needed by the user.

In one or more implementations, the mobile device may be a firstcomputing device. The goods or services may be determined to be goods orservices desired or needed by the user based at least in part on a soundsample detected using a sound sensor. The sound sensor may be a soundsensor of at least one of the first computing device and a secondcomputing device.

In one or more implementations, the first computing device may be asmartphone. The second computing device may be a smart speaker.

In one or more implementations, determining the goods or services asgoods or services desired or needed by the user may comprise analyzingthe sound sample to identify a biometric voice signature of the user.

In one or more implementations, the sound sample may be a first soundsample detected using the first computing device. Determining the goodsor services as goods or services desired or needed by the user maycomprise detecting a second sound sample using the second computingdevice. The goods or services may be determined to be goods or servicesdesired or needed by the user based on both the first and second soundsamples.

In one or more implementations, determining the goods or services asgoods or services desired or needed by the user may comprise analyzingthe first sound sample to identify a first phrase spoken by the user.Determining the goods or services as goods or services desired or neededby the user may also comprise analyzing the second sound sample toidentify a second phrase spoken by the user. Determining the goods orservices as goods or services desired or needed by the user may moreovercomprise determining that the combination of the first and secondphrases indicates that the goods or services are goods or servicesdesired or needed by the user.

In one or more implementations, the first and second sound samples maynot overlap in time.

In one or more implementations, the first and second sound samples maybe detected on different days.

In one or more implementations, the first and second computing devicesmay not be co-located.

In one or more implementations, determining the goods or services asgoods or services desired or needed by the user may comprise detecting afirst sound sample and analyzing the first sound sample to identify afirst issue fragmented issue indicator. Determining the goods orservices as goods or services desired or needed by the user may alsocomprise detecting a second sound sample and analyzing the second soundsample to identify a second fragmented issue indicator. The second soundsample may be non-overlapping in time with the first sound sample.Determining the goods or services as goods or services desired or neededby the user may moreover comprise determining that the first and secondfragmented issue indicators together indicate that the user desires orneeds the goods or services.

In one or more implementations, the first fragmented issue indicator andthe second fragmented issue indicators may be phrases spoken by thefirst user and a second user, respectively.

In one or more implementations, determining the goods or services asgoods or services desired or needed by the user may comprise identifyinga first biometric voice signature of the first user in the first soundsample. Determining the goods or services as goods or services desiredor needed by the user may also comprise identifying a second biometricvoice signature of the second user in the second sound sample.Determining the goods or services as goods or services desired or neededby the user may moreover comprise determining that the first and secondusers are related. Determining the goods or services as goods orservices desired or needed by the user may additionally comprisedetermining that the first and second issue indicators are related.

Various embodiments of the disclosure relate to a mobile device. Themobile may comprise a sound sensor configured to detect ambient sounds.The mobile device may also comprise a location sensor configured todetect a physical location of the mobile device. The mobile device maymoreover comprise a network interface configured to communicate via atelecommunications network. The mobile device may additionally compriseone or more user interfaces configured to present perceptible elements.The mobile device may further comprise a processor and memory havinginstructions that, when executed by the processor, cause the processorto perform specific functions. The mobile device may be configured todetect a physical location of the mobile device based on at least one ofa physical location detected by the location sensor and ambient soundsdetected using the sound sensor. The mobile device may also beconfigured to determine that the physical location corresponds with asource of goods or services. The mobile device may moreover mayconfigured to receive, from a service provider computing system, loandata for a loan for purchasing the goods or services. The mobile devicemay additionally be configured to present perceptible elements using theone or more user interfaces to present the loan as an option for payingfor the goods and services.

In one or more implementations, the instructions may cause the processorto detect a sound sample using the sound sensor. The instructions mayalso cause the processor to determine that the goods or services aregoods or services desired or needed by the user based at least in parton the sound sample.

Various embodiments of the disclosure relate to a service providercomputing system. The service provider computing system may comprise anetwork interface configured to communicate via a telecommunicationsnetwork. The service provider computing system may also comprise aprocessor and memory having instructions that, when executed by theprocessor, cause the processor to perform specific functions. Theservice provider computing system may be configured to determine aphysical location of a mobile device based at least in part on adetected activity. The detected activity may comprise detection of thephysical location of the mobile device using a location sensor of themobile device. The detected activity may alternatively or additionallycomprise detection of a mobile wallet transaction executed via anapplication running on the mobile device and identification of thephysical location of at least one of the entities engaging in the mobilewallet transaction. The detected activity may alternatively oradditionally comprise detection of ambient sounds using a sound sensorof the mobile device and analysis of the ambient sounds to identify asound signature indicative of a physical location. The service providercomputing system may also be configured to generate a loan based atleast in part on the physical location of the mobile device. The serviceprovider computing system may moreover be configured to transmit loandata corresponding with the loan to the mobile device for perceptiblepresentation via one or more user interfaces of the mobile device toindicate the loan as an option for paying for the goods and services.

In one or more implementations, generating the loan may compriseidentifying goods or services that may be acquired at the physicallocation. The loan may be generated for paying for the goods orservices.

In one or more implementations, the instructions may cause the processorto determine that a user associated with the mobile device desires orneeds the goods or services.

In one or more implementations, the user may be determined to desire orneed the goods or services. The user may be determined to desire or needthe goods or services based at least in part on a voice input. The voiceinput may be detected using the sound sensor of the mobile device.

These and other features, together with the organization and manner ofoperation thereof, will become apparent from the following detaileddescription when taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a block diagram of an example computing system framework formajor expenditure advising according to example embodiments.

FIG. 2 is a block diagram for example components that could beincorporated in the computing devices of the computing system frameworkof FIG. 1 according to example embodiments.

FIG. 3 depicts an example method of advising a user according to exampleembodiments.

FIG. 4 depicts an example method of advising a user according to exampleembodiments.

FIG. 5 depicts an implementation of an example pervasive advising systemwith a virtual dashboard according to example embodiments.

FIG. 6 depicts an example profile applicable to advising of one or moreusers according to example embodiments.

FIG. 7 depicts an example data structure for the major expenditure goalsof a customer according to example embodiments.

FIG. 8 depicts an example graphical user interface of a potentialvirtual dashboard according to example embodiments.

FIG. 9 depicts an example communication between a consumer device and aprovider computing device according to example embodiments.

FIG. 10 depicts an example graphical user interface of a potentialvirtual dashboard according to example embodiments.

FIG. 11 depicts an example graphical user interface of a potentialvirtual dashboard according to example embodiments.

FIG. 12 depicts an example graphical user interface of a potentialvirtual dashboard according to example embodiments.

FIG. 13 depicts an example graphical user interface of a potentialvirtual dashboard accessible according to example embodiments.

FIG. 14 depicts example notifications for pervasive advising accordingto example embodiments.

FIG. 15A depicts an example graphical user interface for a possibleinteraction between a customer and advisor according to exampleembodiments.

FIG. 15B depicts an example graphical user interface for a possibleinteraction between a customer and advisor according to exampleembodiments.

FIG. 15C depicts an example graphical user interface for a possibleinteraction between a customer and advisor according to exampleembodiments.

FIG. 16 depicts an example graphical user interface for a potentialvirtual dashboard accessible customers and/or advisors according toexample embodiments.

FIG. 17 depicts an example graphical user interface for an interactionbetween a customer and advisor according to example embodiments.

DETAILED DESCRIPTION

Disclosed are example systems and methods for providing a pervasiveadvisor for major purchases and other expenditures that generallyrequire or otherwise benefit from thoughtful budgeting and planning. Invarious versions, a pervasive advisor may detect that a customer iscontemplating a major purchase (e.g., through active listening andanalyses of conversations). The advisor may assist the customer with thetiming and manner of making the purchase in a way that is financiallysensible in view of the customer's financial situation. In someimplementations, a customer may be provided with dynamically-updatedinformation (in response to, e.g., recent actions that may affect thecustomer's credit score) about an approved loan amount (where the loanrelates to a specific financial goal of the customer, such as buying anew car). The customer may be informed, for example, “Because you justtook action X [to improve your credit score], the interest rate youwould pay is now lower, and for the same total loan cost, you can nowafford to spend $Y more towards your financial goal [buying a new car].”

In various implementations, underwriting of a loan may be triggeredbased on the geo-location of the user (e.g., when the customer is at anew car dealership, at a house that is for sale, at a jewelry dealer,etc.). Financial advice may be provided to customers to help them meettheir goals using information obtained from third party sources (via,e.g., application programming interfaces (APIs) of the third parties).This may include purchase options (e.g., cars available at otherdealers) for a customer who has a particular goal (e.g., purchasing anew car).

The disclosed approach may include a pervasive advisor capable ofintervening to assist with the budgeting and financing of majorexpenditures and with helping the customer to spend what the customercan afford, and at the right time, based on the customer's location andon the customer's unique and changing circumstances. In someimplementations, the pervasive advisor may be structured to generate anexpense strategy. As used herein, the term “expense strategy” may beused to refer to a strategy generated to meet a financial goal. Anexpense strategy may include a financial plan, budget, investmentstrategy, or combination thereof. Example systems may include a consumercomputing device (e.g., a voice activated digital assistant) forreceiving user inputs (e.g., conversations) and for presenting financialinformation via one or more user interfaces. The pervasive advisor may,in certain implementations, provide automated (“robo”) advising usingone or more artificial intelligence and machine learning tools (“AI”)executed on one or more computing devices.

The consumer computing device may be in communication with a providercomputing device of a provider that may be a financial institution. Theconsumer computing device may be structured to detect a voice inputindicative of a financial goal (e.g., a major expenditure, creditissues, transaction, or purchase such as a vacation, new home, expensivejewelry purchase, or any other purchase requiring a substantial orsignificant amount of funds) and receive, via the consumer computingdevice, an expense strategy structured to meet the financial goal inresponse to the detection of the voice input. In some implementations,the expense strategy, or aspects thereof, may be generated by theprovider computing device, the customer computing device, or acombination thereof. The consumer computing device may be structured topresent the expense strategy to a customer associated with the consumercomputing device. In certain implementations, an advisor may beconsulted via an advising session between a consumer computing deviceand an advisor computing device.

In an illustrative example, using inputs acquired using one or moreprovider computing devices, consumer computing devices, and/or advisorcomputing devices, it may be determined that a customer is contemplatingbuying a new car. The fact that the customer is contemplating buying anew car may be learned by applying AI (implemented, e.g., via theprovider computing device and/or the customer computing device) to, forexample, one or more conversations and discussions of the customer. Theinput may be acquired, for example, through active listening by a smartspeaker or other consumer computing device having a microphone, by anadvisor who is assisting with financial planning via the advisorcomputing device, or in another manner.

Through location mechanisms (e.g., GPS devices), the provider computingdevice and/or consumer computing device may detect that the consumercomputing device is, for example, at a new car dealership. Potentially,it may have been several months or more since the consumer computingdevice first detected an expression of an interest in purchasing a newcar (e.g., because the customer is pregnant, and the need for the newcar was not urgent when the customer first found out she was pregnant).The geolocation of the consumer computing device may trigger a loanunderwriting. For example, as a result of having detected that thecustomer computing device is at the new car dealership, the providercomputing device and/or consumer computing device may calculate how muchthe customer can afford to pay for the new car based on the customer'scurrent financial situation. Potentially, the customer's currentfinancial situation may have changed in the interim (e.g., between thetime the expression of interest was first detected and the time thecustomer computing device is detected as being at a car dealership,and/or between the time inputs indicating a customer who drives atwo-seater is expecting a baby and the time the customer visits the cardealership). For example, it may be determined by the provider computingdevice that the customer's credit score may have improved, which wouldentitle the customer to a loan with a lower interest rate thanpreviously available.

The provider computing device may determine that, based on databaserecords of the provider, the customer's internal credit score (e.g.,internal to the provider based on the customer's financial behaviors asa customer of the provider who may be a financial institution), currentbudget, and spending patterns that the customer is approved for a carloan. The provider computing device may, via the consumer computingdevice, provide the customer with the option to accept the loan. Theloan may be underwritten in response to the detection that the consumercomputing device is physically located at a particular place. If theconsumer computing device receives an indication that the customer isnot ready to purchase a new car, the fact that the underwriting occurredbased on internal data of the provider (rather than, e.g., on records ofa third-party credit agency) avoids an impact on the customer's creditscore.

The provider computing device may transmit to the consumer computingdevice a message indicating how much the customer is deemed to be ableto afford based on her current financial situation. The transmittedmessage may advise the customer is pre-approved for a loan at thedetermined amount. If the consumer computing device receives anacceptance, the provider computing device may update the customer'sfinancial plan based on the new loan. Concurrently, the providercomputing device may also access third party APIs of other cardealerships in town or otherwise accessible based on the location of theconsumer computing device. Potentially, at the first car dealership, thecustomer may not find any new cars in her price range that fit herneeds. In one possible scenario, the result may be that the customerpurchases a car that fits her needs but is not in her price range.Instead, however, the provider computing device may have accessed APIsof other nearby car dealerships (identified, e.g., by the providercomputing device as being near the geo-location of the customercomputing device as detected using a location mechanism) and transmitteda message to the consumer computing device that other car dealershipshave cars that meet the customer's needs and that are in her pricerange. As a result, the customer may obtain what she needs at a priceshe can afford based on her financial circumstances, without overpaying.

The disclosed example approach may include a motivational user interfacefeature. For example, the provider computing device may pre-qualify thecustomer for a loan based on the customer's internal credit score (e.g.,financial patterns with the provider), without going to credit agenciesto pull the customer's credit score. As a result, the provider computingdevice could dynamically update the amount for which the customer isapproved each time the provider computing device and/or consumercomputing device engages in a transaction on behalf of the customer. Theprovider computing device may transmit, or otherwise make accessible,the updated amount to the consumer computing device. For example, if thecustomer took an action that had a favorable result on the customer'scredit score (e.g., made an on-time or additional payments towards adebt) via the consumer computing device, the consumer computing devicemay receive immediate favorable feedback in response to the action beingtaken. The feedback provided via the consumer computing device mayoriginate with the provider computing device. As an example message, theconsumer computing device may provide a message via, for example, apop-up notification, access to an application running on the consumercomputing device, or via another messaging mechanism (such as a pop-upnotification or text message stating “With your new lower interest ratedue to your improved credit score, you can now afford a car that costs27,500!”). The immediate positive feedback may be deemed helpful interms of motivating the customer to improve his or her credit score andreaching his or her ultimate financial goal (e.g., of purchasing a newcar, a new home, expensive jewelry (such as a wedding ring), vacation,etc.).

Embodiments and implementations of the systems and methods disclosedherein improve current computing systems by providing proactive andpervasive user experiences for major purchases via dynamically-updatedfinancing options that do not impact a customer's external credit score,and that allow for proactive and versatile loan underwriting in responseto detection of the location of computing devices. In someimplementations, financial goals affecting multiple users may beidentified based on, for example, already-known associations ofcomputing devices of existing customers with a provider computingsystem. Identities may be verified in various ways to prevent fraudulentactivity and to ensure that each person who interacts with the proactivelistening bot operates under the proper security roles and permissions.A “ubiquitous” proactive listening bot (i.e., a bot that may beconfigured to detect signals using multiple or all computing devices ofone or more customers at all times or until turned off or otherwisedeactivated) can be structured to identify financial goals and needssuch as making a large purchase (for which customers often borrowmoney), including goals and needs users may not be able to identify forthemselves due to a lack of information or time to devote.

Further, the disclosed approach improves computing systems by using oneor more computing devices to interact with a user (e.g., a customer) viavoice recognition and analytics that pervasively and interactivelyprovide financial planning advice, loan underwriting, and identificationof suitable options to users. Rather than requiring a user to dedicatetime and computing resources to determining one's financial means (e.g.,how much of a loan the customer may qualify for and at what rate) andresearching available options (e.g., what products meet the customer'sneeds in the short and/or long term), user computing devices can acquirethe information without requiring the user to dedicate time or otherwisechange daily activities. User computing devices are not limited tosingle, one-time statements in determining customer goals and needs(e.g., what car, house, or other major expenditure would be mostsensible based on the customer's familial or other needs and financialcircumstances), but can obtain the needed information over the course ofa day, a week, a month, or longer, based on multiple conversations withfamily and friends, consultations with advisors, and/or otheractivities. By receiving dynamically updated information on availableloans without an impact on external credit score, and receiving loanunderwriting in an automated fashion based on location, the customer cansave time and computing resources while making more informed decisions.Systems, methods, and computer implementations disclosed herein improvethe functioning of such systems and information management by providingunconventional, inventive functionalities that are novel and non-obviousimprovements over current systems.

Referring to FIG. 1 , a block diagram of a major expenditure advisingsystem 100 is shown according to one or more example embodiments. Asdescribed herein, the major expenditure advising system 100 enables theimplementation of pervasive user experiences involving advisingregarding, and timely and flexible loan underwriting for, majorexpenditures. The advising received for major purchases may berobo-advising, human advising, or a combination thereof. As used herein,robo-advising, bot advising, robot advising, and like terms refer toadvising that does not involve interaction with, or intervention by, aperson. Robo-advising may be implemented using one or more mobile ornon-mobile computing devices capable of acquiring inputs from a user(e.g., a user's communications) and automatically performing actions, orproviding recommendations for future actions by the user, that affectthe user's circumstances. The robo-advising may be accomplished using,for example, artificial intelligence tools, intelligent agents, machinelearning, or other logic and algorithms capable of extracting relevantinformation from input streams that include both relevant andnon-relevant information (e.g., conversations that may span multipledays and cover related and unrelated topics).

The major expenditure advising system 100 includes one or more providercomputing devices 110 (of one or more service providers), one or moreconsumer computing devices 120 (of one or more users receiving one ormore financial or other services from the service provider), one or moreadvisor computing devices 130 (of one or more persons who advise users,and who may or may not be associated with the service provider), and oneor more third-party computing devices 140 (of entities that are separatefrom the service provider and that have information relevant to majorexpenditure advising). Each provider computing device 110, consumercomputing device 120, advisor computing device 130, and third-partycomputing device 140 may include, for example, one or more mobilecomputing devices (e.g., smartphones, tablets, laptops, smart devicessuch as home smart speakers and watches, etc.), non-mobile computingdevices (such as desktop computers, workstations, servers, etc.), or acombination thereof.

Provider computing devices 110, consumer computing devices 120, advisorcomputing devices 130, and third-party computing devices 140 may becommunicably coupled to each other over a network 150, which may be anytype of communications network. The network 150 may involvecommunications using wireless network interfaces (e.g., 802.11X, ZigBee,Bluetooth, near-field communication (NFC), etc.), wired networkinterfaces (e.g., Ethernet, USB, Thunderbolt, etc.), or any combinationthereof. Communications between devices may be direct (e.g., directlybetween two devices using wired and/or wireless communicationsprotocols, such as Bluetooth, WiFi, NFC, etc.), and/or indirect (e.g.,via another computing device using wired and/or wireless communicationsprotocols, such as via the Internet). The network 150 is structured topermit the exchange of data, values, instructions, messages, and thelike between and among the provider computing devices 110, the consumercomputing devices 120, the advisor computing devices 130, and thethird-party computing devices 140 via such connections.

Referring to FIG. 2 , computing device 200 is representative of examplecomputing devices that may be used to implement major expenditureadvising system 100, such as one or more provider computing devices 110,consumer computing devices 120, advisor computing devices 130, and/orthird-party computing devices 140. Not every provider computing device110, consumer computing device 120, advisor computing device 130, andthird-party computing device 140 necessarily requires or includes all ofthe example device components depicted in FIG. 2 as being part ofcomputing device 200. Multiple computing devices 200 (each with apotentially different set of components, modules, and/or functions) maybe used by one service provider (e.g., a financial institution providingfinancial and other services), one user (e.g., a customer receivingfinancial advice), one advisor (e.g., a professional who providesfinancial advice suited to a customer's circumstances), or one thirdparty (e.g., a credit agency, government agency, merchant, or othersource of information or provider of services). Similarly, one computingdevice 200 may be used by multiple service providers, multiple users,multiple advisors, or multiple third-parties.

Each computing device 200 may include a processor 205, memory 210, andcommunications interface 215. Each processor 205 may be implemented as ageneral-purpose processor, an application specific integrated circuit(ASIC), one or more field programmable gate arrays (FPGAs), a digitalsignal processor (DSP), a group of processing components, or othersuitable electronic processing components structured to control theoperation of the computing device 200. The memory 210 (e.g., RAM, ROM,NVRAM, Flash Memory, hard disk storage) may store data and/or computercode for facilitating at least some of the various processes describedherein. In this regard, the memory 210 may store programming logic that,when executed by the processor 205, controls the operation of thecomputing system 200. Memory 210 may also serve as one or more datarepositories (which may include, e.g., database records such as user andaccount data and data acquired from various sources). The communicationsinterface 215 may be structured to allow the computing device 200 totransmit data to and receive data from other mobile and non-mobilecomputing devices (e.g., via network 150) directly or indirectly.

Each computing device 200 may include one or more other components(generally involving additional hardware, circuitry, and/or code)depending on the functionality of the computing device 200. Userinterfaces 220 include any input devices (e.g., keyboard, mouse,touchscreen, microphone for voice prompts, buttons, switches, etc.) andoutput devices (e.g., display screens, speakers for sound emission,notification LEDs, etc.) deemed suitable for operation of the computingdevice 200. Computing device 200 may also include one or more biometricscanners 225, such as fingerprint scanners, cameras for facial, retinal,or other scans, microphones for voice signatures, etc. In conjunctionwith, or separate from, the biometric scanners 225, each computingdevice 200 may include authentication circuitry 230 to allow thecomputing device 200 to engage in, for example, financial transactions(such as mobile payment and digital wallet services) in a more securemanner. Various computing devices 200 may include one or more locationsensors 235 to enable computing device 200 to determine its locationrelative to, for example, other physical objects or relative togeographic locations. Example location sensors 235 include globalpositioning system (GPS) devices and other navigation and geolocationdevices, digital compasses, gyroscopes and other orientation sensors, aswell as proximity sensors or other sensors that allow the computingdevice 200 to detect the presence and relative distance of nearbyobjects and devices. Computing device 200 may also include ambientsensors 240 that allow for the detection of sound and imagery, such ascameras (e.g., visible, infrared, etc.) and microphones, in thesurroundings of computing device 200. A computing device's microphonemay be considered an ambient sensor that could also be used as abiometric scanner if it is involved in capturing the voice of a user forauthentication purposes, and/or a user interface if the microphone isinvolved in receiving information, commands, or other inputs from, forexample, speaking users.

Each computing device 200 may include one or more applications 250(“apps”) that aid the computing device 200 in its operations and/or aidusers of the computing device 200 in performing various functions withthe computing device 200. In some implementations, applications 250 maybe stored in memory 210 and executed using processor 205, and mayinteract with, or otherwise use, one or more of communicationsinterfaces 215, user interfaces 220, biometric sensors 225,authentication circuitry 230, location sensors 235, and/or ambientsensors 240. Not every provider computing device 110, consumer computingdevice 120, advisor computing device 130, and/or third-party computingdevice 140 necessarily requires or includes all of the exampleapplication components/modules depicted in FIG. 2 as being part ofapplication 250.

Example components of one or more applications 250 (running on, e.g.,provider computing device 110, consumer computing device 120, and/oradvisor computing device 130) include an underwriting engine 255configured to underwrite loans for major expenditures. The loanunderwriting may be based on a customer's financial circumstances,goals, needs, and plans, as well as the location of a computing device200 associated with the customer. For example, the underwriting engine255 (running on provider computing device 110 and/or consumer computingdevice 120) may use location sensors 235 to determine that a customer islocated at or moving towards a location corresponding with a customer'sdesire or intent to engage in a transaction related to a majorexpenditure. The location may be, for example, a car dealer, a for-salehouse, an office of a realtor or agent for houses being newlyconstructed (which may be, e.g., a model home for a new housingdevelopment), a jewelry store, a travel agent, etc. The customer'sdesire or intent to engage in a transaction may be based on variousinputs, such as conversations of the customer detected using ambientsensors 240. The underwriting engine 255 may determine that a desiredpurchase may be procured at the location based on, for example,information on the location acquired via access to third-party computingdevices 130. For example, a directory may be accessed, or a website of abusiness in the vicinity of the consumer computing device 120.

In some implementations, the computing device 200 (e.g., providercomputing device 110 and/or consumer computing device 120) may firstidentify a location at which a customer may make a major purchase, andthen cross-reference the sort of items available at the location withthe customer's desires, intent, goals, and/or needs (which may be, ormay have been, determined using inputs previously acquired via thecomputing device 200). For example, the consumer computing device 110may, as a matter of course, check on its location using its locationsensors 235 periodically. The frequency of the location checks may bebased on various factors, such as: expressions of intent (e.g., thecustomer expresses a desire to visit a car dealership in a conversationwith a friend or family member); whether the consumer computing device120 is detected to be moving above a certain velocity (e.g., whiledriving 25 miles per hour (mph) or faster, while walking 5 mph or fasteron foot, etc.); whether the consumer computing device is moving in acertain direction (e.g., towards a relevant location corresponding witha transaction point involving a major expenditure); etc.

For example, in certain implementations, the consumer computing device120 may determine that the current date corresponds with a day on whichthe customer specifically expressed a desire or intent to visit a cardealer or other transaction point. Similarly, the consumer computingdevice 120 may determine that it is currently a non-work day or non-workhour after the customer expressed a general desire or intent to visitone or more car dealers. That it is a non-work day or non-work hour maybe determined in various ways. For example, in various implementations,it may be determined that it is a non-work day or non-work hour usingcustomer data available to the provider computing device 110, variousinputs from the customer, and/or information from third party computingdevices 140, such as social networking sites. The consumer computingdevice 120 may, in such circumstances, ping location sensors 235 morefrequently than on other days or times. Alternatively or additionally,while the consumer computing device 120 is detected to be moving atleast a certain speed (e.g., 25 mph, 35 mph, 55 mph, etc.), location maybe checked more frequently than when the consumer computing device isnot moving or is moving more slowly (because, e.g., it may be presumedthat if the consumer computing device 120 is moving, it could headtowards location at which a major purchase may be made). Similarly, ifthe consumer computing device 120 is traveling towards a commercialdistrict known to have merchants relevant to a customer goal, then theconsumer computing device 120 may ping location sensors 235 morefrequently than it would otherwise. Example frequencies include once ortwice a minute, every 5 minutes, every 30 minutes, or other intervalsdeemed suitable.

If the provider computing device 110 and/or consumer computing device120 determines that the consumer computing device 120 is approaching arelevant location (e.g., is entering a car dealership or othertransaction point such as a merchant, a for-sale house, etc.), theunderwriting engine 255 (e.g., an application 250 running on theprovider computing device 110) may determine a suitable amount andinterest rate for a loan to the customer for a major expenditurecorresponding to the location. The suitable amount and interest rate maybe determined (by, e.g., the provider computing device 110) based atleast in part on what the major expenditure is and on the financialcircumstances and behavior of the customer. If the provider is afinancial institution and the customer has accounts with the provider,or if the provider otherwise has access to a sufficient amount ofinformation related to the customer's financial information andbehaviors, the provider computing device 110 may determine the loanamount and interest rate based on internal data. Advantageously, by notaccessing the customer's credit score as determined by a credit agency,and instead using internal data, underwriting of the loan need notimpact the customer's external credit score.

An advisor manager 260 may be configured to determine whether or when itis advisable to transition a user between robo-advising and humanadvising based on one or more transition triggers (which are furtherdiscussed below). For example, the advisor module 260 (running onprovider computing device 110 or consumer computing device 120) may useinputs to determine that it is appropriate to transition a usercomputing device 120 from robo-advising to human advising based on oneor more human advising triggers, and from human advising torobo-advising based on one or more robo-advising triggers. Such“go-human” triggers may indicate that a need or goal of a user issufficiently complex, variable, unpredictable, or significant so as towarrant input from or review by a human advisor. For example, humanadvising triggers may indicate that two or more options are availablefor a user, with the options sufficiently divergent (i.e., havingsubstantially different consequences depending on factors beyond thepurview of the robo-advisor, and/or requiring subjective evaluation of auser's circumstances) to warrant human intervention. Example go-humantriggers may include: a transaction exceeding a threshold value (e.g.,purchasing a car or home of at least a certain value); a conversationdetermined to indicate that a situation is very emotionally charged(based on, e.g., above-average volume for the voice of the speakers,detection of tension in voices, and/or identification of a major lifeevent); extensive communications about a topic, suggesting that the useris weighing many factors because a major expenditure is significantlynuanced or particularly personal; use of predetermined keywords orphrases associated with topics outside the purview of the robo-advisor;expression of a desire to speak with a professional advisor; etc.Go-human triggers may be identified in, for example, conversations orother communications of the customer with other users and/or with achatbot.

Similarly, the advisor module 260 (running on, e.g., provider computingdevice 110, user computing device 120, and/or advisor computing device130) may determine, during a communications session between a customerand an advisor, that the customer may have reached a point that nolonger requires human intervention, or that a return to robo-advisingmay otherwise be a viable option, based on one or more triggers forrobo-advising. Such “back to bot” triggers may, for example, indicatethat the motivation for transitioning to human advising may no longer berelevant (e.g., an issue has been resolved or otherwise sufficientlyaddressed, one or more accounts have been set up and/or restructured,etc.), that the topics being discussed are all in the purview of therobo-advisor, and/or that the conversation has become non-financial innature (e.g., the user and advisor have concluded a discussion of lifeevents or financial situations and are only discussing news or sports).In some implementations, if the topics being discussed during ahuman-advising session have no go-human triggers (such that if thediscussion had been detected outside of the session with the advisor,the robo-advisor would not have determined that human intervention orreview is warranted), then the advisor manager 260 may determine that areturn to robo-advising is appropriate. Back-to-bot triggers may beidentified in, for example, conversations or other communications of thecustomer with the advisor, such as entries while interacting with a userdashboard during a session with the advisor.

The advisor manager 260 may further be configured to identify one ormore advisors that may be able to assist a user based on the user'sneeds and/or goals, and to schedule a meeting or other communicationssession with the advisor (by, e.g., comparing the user's and advisor'scalendars to determine mutual or overlapping availability). For example,if the provider computing device 110 and/or consumer computing device120 determines that the customer would likely benefit from humanadvising, or it is otherwise determined that the major expenditure(e.g., buying a home) is well suited for human advising, the advisormanager 260 may access records stored at a provider computing device110, an advisor computing device 130, and/or a third-party computingdevice 140 to determine which advisors may have the background andexperience suited to the customer's needs and goals. The advisor manager260 may also access records (e.g., transcripts) of prior sessions of anadvisor (with the same or with other users) to determine whether theadvisor would be a good match with the user of the consumer device 120.The ultimate suitability of an advisor may sometimes be based, at leastin part, on whether the calendars reveal mutual/overlapping availabilityfor the consumer and the advisor (even if otherwise matched based onneeds and expertise). The advisor manager 260 may access one or morecalendars accessible to one or more consumer devices 120 to determinethe customer's availability. In some implementations, the advisormanager 260 may determine the customer's availability based ondiscussions of the user (e.g., detecting via a consumer device 120 thatthe customer stated “I'm available all day Friday”) or othercommunications. The advisor manager 260 may access one or more calendarsaccessible to provider computing device 110, advisor computing device130, and/or third-party computing device 140 to determine theavailability of one or more advisors. Computing devices withseparately-maintained calendars may interface with each other using,e.g., any combination of one or more application programming interfaces(APIs), software development kits (SDKs or devkits), or otherhardware/software mechanisms that facilitate data exchange orcommunication between and among co-located or remote computing systemswith various access protocols.

A location monitor 265 may be configured to determine the location of,for example, consumers and advisors, as well as the locations associatedwith customer transactions (e.g., where a transaction took place). Thelocation monitor 265 may be configured to track (using, e.g., one ormore location sensors 235) the physical location of computing device200. The location monitor 265 may be configured to identify the locationof the computing device 200 at specified points in time or whentriggered by identified events, such as the location of the consumercomputing device 120 when a purchase occurs, when a device is turned onor off, when an application is launched, etc. The location of computingdevice 200 may be presumed to correspond with the location of one ormore users associated with the computing device 200, and/or the locationat which an event occurred. In different implementations, location maybe determined without using location sensors 235. For example, locationof computing device 200 may be determined by determining the location ofa merchant at which a purchase occurred using a payment app running oncomputing device 200. Additionally or alternatively, location may bedetermined using other sensors, such as ambient sensors 240 used todetect sounds and videos that are recognized as indicative of a certainphysical location of the computing device 200 (e.g., detection of spokenwords or phrases from which location may be inferred, or detection ofsounds from a public announcement system of a particular landmark suchas a train station or airport). Also, a location of a first computingdevice may be determined based on (geographically-limited)communications (such as NFC, Bluetooth, WiFi) of the first computingdevice with a (nearby) second computing device (such another user'ssmartphone, the router of a hotel or restaurant, etc.) for whichlocation has already been determined or is known or presumed.

A chatbot 270 may be configured to simulate a conversation between acustomer and advisor. Such a conversation may be conducted by, forexample, capturing a customer's spoken words (or other communications),analyzing the communication to better understand context and identifyuser needs, and responding to the customer or otherwise providinginformation determined to be relevant. In some implementations, inputs(or a portion thereof) received via chatbot 270 may be fed to analyticsengine 275 for analyses and formulation of responses. Alternatively oradditionally, chatbot 270 may perform the analyses needed to formulatesuitable responses to users. In certain implementations, certainanalyses may be performed by chatbot 270 (e.g., determining what a useris asking and identifying when a financial issue has arisen), whileother analyses (e.g., determining what recommendation would be suitablebased on the financial issue and the user's circumstances, behaviors,etc.) may be performed via analytics engine 275.

The analytics engine 275 may be configured to enable artificial/machineintelligence capabilities by, for example, analyzing customer andadvisor inputs (to, e.g., determine user goals and needs) and generatingrecommendations and proposals for presentation to the customer (to,e.g., achieve goals and/or satisfy needs). The analytics engine 275 mayutilize, for example, artificial intelligence and machine learning toolsto analyze customer conversations or other inputs and otherwise providerobo-advising without human intervention.

A transaction monitor 280 may be configured to identify and keep trackof financial or other transactions of users. A customer may engage intransactions using, e.g., mobile payment and digital wallet services, orvia any app and/or device through which a user may make purchases,transfers, deposits, cash advances, etc. The transaction monitor 280 mayaccess such sources as user accounts (e.g., bank accounts, brokerageaccounts, credit card accounts, merchant accounts, etc.) andpayment/wallet applications to acquire data on transactions. Transactiondata acquired via transaction monitor 280 may be used, for example, byanalytics engine 275 to determine progress towards a goal of making amajor purchase, and/or by underwriting engine 255 to determine how muchof a loan (and at what interest rate) the customer may qualify forwithout knowing a credit score from a credit agency.

A session manager 285 may be configured to initiate and terminatecommunications sessions between consumer computing devices 120 andadvisor computing devices 130. Such advising sessions may incorporateone or more of audio, video, and text entries of users and advisors. Insome implementations, advising sessions may be conducted via the samedashboard (e.g., from within the same application) through which theuser is robo-advised. Advising sessions may begin at times scheduled viaadvisor manager 260, and/or on an ad-hoc basis.

A score generator 290 may generate and update internal customer scoresfor use in determining creditworthiness, progress towards a goal ofmaking a major purchase, etc. In various versions, the score generator290 may use data stored at or accessible to the provider computingdevice 110 (e.g., account balances, purchase history, indebtedness,etc.) to evaluate how much of a loan the customer could afford and whatinterest rate may be warranted. In some implementations, the scoregenerator 290 may use, for example, data from transaction monitor 280 toevaluate the spending habits of a customer. Data from third partycomputing devices 140 may also be used, such as financial informationaccessible to other financial institutions, credit scores acquired fromcredit agencies, etc. The score generated by score generator 290 coulduse any scale and have any ranges or categories deemed useful. Incertain implementations, the score may be used by the underwritingengine 255 to determine a suitable amount and interest rate for, forexample, a car or other loan to the customer.

An external resource module 295 may be configured to access data frominformation sources other than the provider computing device 110 and theconsumer computing device 120. In some implementations, the externalresource module 295 may use, for example, any combination of one or moreAPIs, SDKs, or other hardware/software mechanisms that facilitate dataexchange or communication between and among co-located or remotecomputing systems with various access protocols. Alternatively oradditionally, the external resource module 295 may accesspublicly-available information sources. External resources may includefinancial product websites, merchant websites, and other sources ofinformation on available products (e.g., alternative majorexpenditures). In certain implementations, the external resource module295 may access social networking websites for information on, forexample, life events and familial or other relationships to understand(in an automated fashion) the needs, circumstances, and likely goals ofa user (e.g., information on who might be affected by the financialdecisions of a user, such the user's children). The external resourcemodule 295 may similarly access other sources of information, such ascredit agencies, news sources, financial institutions, governmentalbodies, etc. Information from such sources may provide inputs to theanalytics engine 275 to inform the robo-adviser in makingrecommendations as to, for example, financial goals and changes thereto.The information may also be made available to human advisors to assistwith advising sessions.

Although the above discussion identifies a set of modules that performspecified functions, in various implementations, the above (and other)functions may be performed by any module in the system 100. Functionsperformed by the modules discussed above may be redistributed (i.e.,differently apportioned or distributed) among the modules ofapplications running on provider computing devices 110, consumercomputing devices 120, advisor computing devices 130, and/or third-partycomputing devices. Similarly, the functions discussed may beconsolidated into fewer modules, or expanded such that they areperformed by a greater number of (separate) modules than illustratedabove. For example, functions performed by the above-identified modulesof one or more provider computing devices 110 could additionally oralternatively be performed by modules of one or more consumer computingdevices 120, and functions performed by the above-identified modules ofone or more consumer computing devices 120 could additionally oralternatively be performed by modules of one or more provider computingdevices 110.

Referring to FIG. 3 , various versions of example process 300 may beimplemented using, for example, a provider computing device 110, aconsumer computing device 120, and an advisor computing device 130. At310, one or more computing devices 200 (e.g., consumer computing devices120 and/or, in some implementations, provider computing device 110) maybe used to capture user inputs. User inputs may include conversations(e.g., spoken conversations or discussions in electronic messages)captured via computing devices 200, entries submitted via application250, or any other transfer or exchange of data from the user to thecomputing device 200 and/or between computing devices 200. For example,application 250 running on consumer computing device 120 may detect(using microphones of one or more consumer computing devices 120) that acustomer is discussing a major expenditure. In some implementations, aprovider computing device 110 may receive audio of a conversation from aconsumer computing device 120 for analysis, and/or a consumer computingdevice 120 may itself analyze audio of conversations. In certainimplementations, particular keywords or phrases may be deemed toindicate a potential desire or need for a major expenditure. Examplesinclude: “We're going to need a bigger car”; “I would like to go on avacation/I need a vacation”; “Honey, we need a house with more bedroomswith another kid on the way/with your mother moving in”; etc.

Additionally or alternatively, at 320, one or more computing devices 200may access records on financial or other transactions of the user toidentify transactions indicative of a user need or goal (such as babysupply purchases indicative of a growing family and a potential goal orneed to purchase a new car). In some implementations, such transactionsmay be detected via, for example, application 250 running on, forexample, a consumer computing device 120, such as mobile wallet orelectronic payment application. In various implementations, suchtransactions may be identified by, for example, a consumer computingdevice 120 accessing user records maintained at or administered by aprovider computing device 110 (e.g., for accounts held at a providerthat is a financial institution) and/or accessing a third partycomputing device 140. In some implementations, such transactions may beidentified by a provider computing device 110 accessing a consumercomputing device 120 and/or a third party computing device 140.

At 330, one or more computing devices (e.g., provider computing device110 and/or consumer computing device 120) may retrieve data from thirdparty computing devices 140 that may be informative of a user'scircumstances. For example, accessing a social medial account mayindicate that a customer is getting married and may need to purchase awedding ring, or that a customer is planning a vacation. Similarly,application 250 (running on, e.g., a provider computing device 110and/or a consumer computing device 120) may access social networkingapplications to identify family members, life events, etc. Adetermination as to which third party data sources to access may bebased at least in part on user inputs and/or transactional data. Forexample, application 250 may detect a conversation about an upcomingtrip without an identification of the destination, or about an upcomingmove to a new city without an identification of the city, and inresponse a provider computing device 110 may determine that accessing athird party computing device 140 of a social networking or other sourcemay help identify the destination or new city.

At 340, the user inputs, transactional data, and/or third party data maybe analyzed by one or more computing devices 200 (e.g., via analyticsengine 275 of application 250 running on a provider computing device 110and/or on a consumer computing device 120) to identify one or more majorexpenditures and how much the customer may be able to afford to spend onthe major expenditures. For example, based on user inputs acquired via aconsumer computing device 120, a provider computing device 110 maydetermine that a customer wishes to buy a new car. In response, at 350,the provider computing device 110 may present, via an application 250running on a consumer computing device 120, a list of available carsthat would be suited to the customer's family size. The options mayinclude, for example, minivan or other family car options available fromvarious car dealerships. In some implementations, if it is determinedthat the major expenditure warrants review by or discussion with a humanadvisor, the provider computing device 110 (presented via, e.g.,application 250 running on a consumer computing device 120) mayrecommend engaging with a human advisor (e.g., an advisor generally, anadvisor by specialty or expertise, and/or an advisor by name). Theadvisor manager 260 running on, for example, a provider computing device110 and/or a consumer computing device 120 may then help the consumercomputing device 120 find and connect with one or more advisor computingdevices 130.

At 360, in various implementations, the consumer computing device 120provides advice on the major expenditure. The advice may includerecommendations for, for example, changing spending habits, paying offdebt at a faster rate, opening savings or other accounts, transferringfunds, using lower-rate credit cards, deferring a purchase, etc. Basedon the customer's financial circumstances and subsequent actions (e.g.,level of debt, balances in savings accounts, etc.), the providercomputing device 110 may present (via, e.g., an application 250 runningon the consumer computing device 120) how much the customer could affordat that time and what interest rate would apply if, for example, a loanwere to be underwritten that same day, same hour, or other timeframe. Invarious implementations, the amount and/or interest rate would beupdated in real-time or otherwise following every action of which aprovider computing device 110 becomes aware. This allows, for example, acustomer to see the impact of every action (e.g., making a payment, apurchase, a funds transfer, etc.) on how much the customer could affordto spend on a major purchase. Because the amount could be based on theinternal score generated by score generator 290 running on providercomputing device 110 (rather than on, e.g., a credit score from a creditagency), affordability could be determined more quickly and as a resultof each action without waiting for the action to trickle through to thecredit agency and its credit score determination.

If or when a customer wishes to proceed with the major purchase,computing device 200 (e.g., provider computing device 110 and/orconsumer computing device 120) may, at 370, monitor the customer'slocation (via, e.g., location monitor 265). The provider computingdevice 110 may then (via, e.g., underwriting engine 255) underwrite aloan based on the location. For example, if the consumer computingdevice 120 is determined to be located at or approaching a car dealer,the underwriting engine 255 may underwrite a loan for a car for theamount that the customer could afford. Information on the seller (i.e.,the recipient of the loan disbursement) could be determined by, forexample, cross-referencing the location of the consumer computing device120 with business information acquired by provider computing device 110via one or more third party computing devices 140.

Referring to FIG. 4 , an example process 400 for providinglocation-independent advising 410 (on left side) and location-dependentadvising 420 (on right side) is depicted. At 430, provider computingdevice 110 surveils consumer computing devices 120 and third partycomputing devices 140 to identify a financial goal of making a majorpurchase. As discussed above, this may be accomplished, for example, viachannels that allow for monitoring of communications (e.g., by detectingconversations via a chatbot and/or scanning electronic messages toextract relevant data). In other implementations, the major expendituremay be identified based on a location (i.e., not location independent),such as a location of the provider computing device 110 as determinedusing a location sensor, and/or based on a location of a transaction(e.g., a location of a merchant at which a purchase was recently madeusing a mobile wallet application running on the provider computingdevice 110). At 435, provider computing device 110 and/or consumercomputing device 120 may monitor transactions, behaviors, patterns,actions, etc. that could affect the customer's internal score orotherwise the amount that the customer could afford. At 445, theprovider computing device 110 (via, e.g., application 250 running on theconsumer computing device 120) may present one or more recommendationsfor achieving the goal of making the major purchase. The consumercomputing device 120 may also present options (e.g., available cars,homes, jewelry, etc., acquired via third party computing devices 140)and up-to-date information on how much the customer is deemed to be ableto afford (e.g., how much of a loan would currently be underwritten bythe provider and at what interest rate).

At 450, the provider computing device 110 and/or consumer computingdevice may determine the presence of the consumer computing device 110at a transaction point (e.g., a point of sale for major expenditures,such as a car dealership). At 455, underwriting engine 255 (running on,e.g., provider computing device 110) may underwrite a loan for the majorpurchase corresponding to the transaction point and to the customer'sgoals and needs. Alternatively or additionally, at 460, the providercomputing device 110 and/or consumer computing device 120 may presentother options (e.g., other cars available at the same or other dealers)that meet or exceed the customer's needs and that do not cost more thanthe amount deemed affordable for the customer. Once the alternativeshave been presented, at 450, the location of the consumer computingdevice 120 would again be monitored to determine if the customer has,for example, left one dealer and is headed to another dealer. Inresponse to detection at a new transaction point, at 545, a new loan maybe underwritten corresponding to a purchase from, for example, thealternative dealer. At 465, the purchase may be completed and theprovider computing device 110 and/or consumer computing device 120 mayupdate the customer's profile to reflect the purchase.

Referring to FIG. 5 , in example implementations, a system 500 mayinclude a virtual dashboard 510 (see, e.g., FIGS. 8-17 discussed below)that is accessible to one or more consumer computing devices 120 and oneor more advisor computing devices 130. The dashboard 510, which may bemaintained and/or administered using one or more provider computingdevices 110 of a service provider, may be “unified” in the sense that itallows consumer computing devices 120 and advisor computing devices 130to effectively exchange information in the same virtual environment.Because customers and advisors may interact with each other via, forexample, user interfaces with common elements, and both users andadvisors may be able to readily access at least some (if not all) of thesame information and user interface elements, advisors may more easilylearn of a customer's circumstances (goals, needs, etc.) via dashboard510. This may help save consumers and advisors from needing to devote asubstantial amount of resources (time, computing resources, etc.) tobring an advisor “up to speed.” Users need not spend time explainingtheir unique situations by sharing details that have already beenentered or otherwise provided by the user or acquired from variousinformation sources (such as third-party computing devices 140). Acommon dashboard helps discussions by allowing customers and advisors torefer to the same user interface elements. Moreover, familiarity withthe dashboard allows the customer and advisor to more readily access andprovide information that is relevant to different topics being discussedor otherwise addressed. The unified dashboard 510 may help provide forsmoother transitions between robo-advising and human advising.

In certain implementations, the provider computing system 110 maymaintain a user profile (further discussed below) that may includerelevant financial information, user preferences, triggers fortransitioning between robo-advising and human advising, and other data.The provider computing system 110 may use user profiles to assist withthe implementation of dashboard 510. Consumer computing devices 120 canbe provided access to the dashboard 510 to receive recommendations,review conversations, enter additional information, monitor progresstowards goals, request and schedule human advising sessions, etc.Advisor computing devices 130 may be used to access consumer data,schedule advising sessions with consumers, provide additionalrecommendations, monitor and update goals, etc. The user profile mayinclude parameters for what information is accessible, when transitionsare advisable, etc., further helping make transitions smoother.

Referring to FIG. 6 , illustrated is an example profile 600 that may, incertain implementations, be generated and/or maintained by providercomputing devices 110 for use by provider computing devices 110,consumer computing devices 120, and/or advisor computing devices 130.This profile may be saved in memory as database records, data packets,text files, or in other suitable formats.

As discussed above, an advisor module 260 may determine that it isappropriate to transition a user computing device 120 from robo-advisingto human advising to better assist a customer. To facilitate suchdeterminations, profile 600 may include go-human triggers 605 (discussedabove) to assist with the identification of a situation in which a humanadvisor may be suitable. Go-human triggers 605 may, for example, beunique to the specific customer based on past behaviors (e.g., if acustomer has sought human assistance when a certain issue arises or withrespect to certain major purchases, the issue/behavior may indicate ago-human trigger 605). Triggers 605 may also include customer inactionin response to certain life events (e.g., a growing family) and/or inresponse to certain recommendations in situations (which may be uniqueto a customer) deemed to be significant enough to warrant action soonerrather than later (based on, e.g., certain detected inputs).

Similarly, the advisor manager 260 may determine a return torobo-advising may be appropriate based on back-to-bot triggers 610(discussed above). Back-to-bot triggers 610 may be based on, forexample, certain behaviors of the customer. For example, if a customeris detected to routinely (and in a sufficiently timely manner) handlecertain financial situations without advising sessions with advisorcomputing devices 130, then identification of the financial situationmay be a back-to-bot trigger that indicates it may be suitable to allowthe customer to continue on a robo-advising track or otherwise withouthuman discussion for the time being. Back-to-bot triggers mayalternatively or additionally be based on a customer's savviness,expertise, or familiarity with certain situations. For example, if acustomer is determined to be sophisticated with respect to certainfinancial situations, then identification of the corresponding financialsituations may indicate that robo-advising may be suitable. In someimplementations, a customer's savviness or ability to handle a situationmay be determined, for example, via an evaluation (e.g., using analyticsengine 275 running on provider computing device 110, consumer computingdevice 120, and/or advisor computing device 130) of the customer'ssophistication with respect to certain issues. Sophistication may bebased on, for example, how advanced the language used by the customer iswith respect to an issue. For example, a customer who is detected todiscuss available options with respect to a certain financial situationwith a family member may be deemed more sophisticated than a customerwho is detected only to discuss the circumstances of the financialsituation with no talk of viable options for how the customer mayproceed. Sophistication (in general or specific to financialissues/situations) may be stored in one or more fields of profile 600 tohelp with advising generally and to help make transitions betweenrobo-advising and human advising more effective.

In certain implementations, fragmented issue indicators 615 may be usedto allow provider computing device 110 and/or user computing device 120to track and connect inputs over time (as being related or otherwise asbuilding upon each other to form a better picture of circumstances orotherwise better inform advising). In some situations, a person's needsor goals do not become apparent in one conversation, statement,communication, transaction, or other act. For example, the keywordsand/or phrases that indicate a user has a certain need or goal may notbe detected as part of a single conversation or otherwise within a shortperiod of time. Needs or goals may unravel over time (hours, days,weeks, months, etc.) as a consumer obtains more information and/orcontemplates his or her situation based on new events and availableinformation. And the bases for such goals and needs may go unexpressedor otherwise remain unapparent for some time.

For example, a consumer computing device 120 may detect a customerexplaining to a friend that his or her mother had a bad fall, and maydetect, in a separate conversation with his or her sibling, the customerexplaining “she's going to need someone to look after her.” And theconsumer computing device 120 may detect a third conversation with thecustomer explaining “she's moving in,” and/or a fourth conversationregarding the house not being large enough and/or not having a room onthe main level (so that the mother could use a room that does notrequire climbing stairs). Separately, these inputs may be insufficientto identify a financial goal or need and make a good recommendation.However, when considered together, these two inputs may be deemed (by,e.g., analytics engine 275) to indicate that a user may need certainfinancial assistance or have a certain financial goal (such as a needfor a larger house, or one with a main-level bedroom, because the motheris moving in).

The consumer computing device 120 (and/or the provider computing device110 using audio or other data received via consumer computing devices120) may (based on, e.g., detected keywords, phrases, or other signals)determine that a piece of information may potentially be relevant towhether a financial goal or need exists. If such a signal is detected,the provider computing device 110 and/or user computing device 120 mayrecord such a signal as a fragmented issue indicator 615. Then, when asecond (or third, fourth, etc.) signal that is similarly determined toinclude a piece of information that is potentially relevant to somefinancial issue is detected, the provider computing device 110 and/orconsumer computing device 120 may access profile 600 for fragmentedissue indicators 615 that may be relevant. If such a related fragmentedissue indicator 615 is in the user's profile 600, the robo-advisor (via,e.g., the provider computing device 110 and/or the consumer computingdevice 120) may determine that there is a likely need, and generate anappropriate recommendation, may determine that more information (e.g.,additional signals or inputs) is needed to generate a relevant or usefulrecommendation.

In the above example, the consumer computing device 120 and/or providercomputing device 110 may identify a first signal when a phrase such as“my mother had a bad fall last night” is detected. In someimplementations, application 250 may first process the signal to givethe signal more meaning or clarity and/or to supplement the signal withadditional information. For example, analytics engine 275 running onprovider computing device 110 may analyze the phrase and retrieveinformation from various sources to determine who was involved (e.g.,who is the speaker's mother based on user records or third partysources), on what date the fall occurred (e.g., what is the date of theday before the day on which the signal was detected), what can bepredicted about the fall in the context of the conversation (e.g., ifthe speaker's voice indicated that the speaker was upset, the fall maybe deemed to have been more serious or more recent than if the speaker'svoice indicated the speaker was apparently nonchalant about theincident), what a “bad” fall might mean for a person of the mother's ageor other known or determinable circumstances (e.g., the mother's age orwhether such falls have occurred in the past), etc. Such information maybe in the user's record or determinable from third party sources (e.g.,from sources of medical information), and the fall may be deemed moreserious based on certain criteria (such as the mother's age being abovea certain age threshold, the mother suffering from certain conditionsassociated with low bone density, etc.). In various implementations,signals (detected via, e.g., provider computing device 110 and/orconsumer computing device 120) need not be limited to expressions (e.g.,spoken conversations or other discussions). Additionally, signals may beactions taken (using, e.g., consumer computing device 120), such asopening certain accounts, making certain funds transfers, making certainpurchases, and/or traveling to certain locations (such as cardealerships, open houses, baby supply stores, assisted living homes,hospitals in general, specific clinics or doctors' offices with certainspecialties, accountants' offices), etc.

The provider computing device 110 and/or consumer computing device 120may record a fragmented issue indicator 615 following the first signalin the profile 600. In various implementations, fragmented issueindicator 615 may state, for example, a derivation of the phrase (e.g.,“family member had an accident,” “user's mother had a fall,” etc.), thephrase itself (i.e., “my mother had a bad fall last night”), or asupplemented or otherwise revised version of the phrase (e.g., “mymother had a bad fall [on mm/dd/yyyy],” “[user name's] ‘mother had a badfall’ on mm/dd/yyyy,” or “[mother's name] ‘had a bad fall’ onmm/dd/yyyy”).

Where the fragmented issue indicator 615 arises from detection of alocation of the consumer computing device 120, the fragmented issueindicator 615 may include an identification of the location visited,such as “customer visited open houses at [home 1] and [home 2]” or“customer visited assisted living home [at address].” In someimplementations, the identification of the location may be accompaniedby an indication of the amount of time spent at the location, such as“customer spent [amount of time] at an assisted living home.” In certainimplementations, a visit to a location may not be deemed significantenough to warrant recording a fragmented issue indicator unless theconsumer computing device 120 was detected to have remained at thelocation for a certain minimum amount of time. For example, a fragmentedissue indicator 615 may not be triggered unless the consumer computingdevice 120 was detected to have remained at a relevant location aminimum of 10 minutes. In some implementations, an analytics engine 275may decide whether to include a fragmented issue indicator 615 inprofile 600 by balancing the likely relevance of a statement or alocation visited, the amount of time spent at the location, and/or thelikely impact on advising or needs and goals of the customer.

In some versions, fragmented issue indicators 615 may be saved as acompilation of, or otherwise associated with, multiple fields. Forexample, there may be “subject” or “primary” field that may be populatedwith a phrase or derivations thereof, identification of certain actions,or other signals. Additional example fields include: time and/or date aninput was captured and/or added to profile 600; which computing devicewas used to capture an input; identity of a user associated with thecomputing device used to capture an input; location of the computingdevice used to capture an input; identify of the speaker or source ofthe input; etc. In some implementations, these may be used to givemeaning to fragmented issue indicators 615 or combinations thereof.

In some implementations, a user's profile 600 includes fragmented issueindicators 615 associated with multiple users. The names of other users(e.g., family members, confidants, etc.) with whom a user is associatedmay be included in profile 600 (e.g., in goals and progress 625), andfragmented issue indicators 615 may be stored in multiple profiles 600such that any single profile 600 may include the fragmented issueindicators 615 of all associated users. For example, a first user'sprofile 600 may include fragmented issue indicators 615 of a second user(and vice versa) who is a family member, friend, or otherwise associatedwith the first user. Signals acquired from multiple individuals (storedin one or more profiles 600) may then be used by, for example, providercomputing device 110 and/or consumer computing device 120 to generaterecommendations.

As an illustrative example, a first signal may be based on a first inputresulting from a first user (e.g., an adult child) saying “we're goingto need to look after her.” A second signal may be based on a secondinput from a second user (e.g., a parent of the adult child) saying “Ihad a bad fall.” A third signal may be based on detection of theconsumer computing device 120 being located at an open house for a housethat is larger than the customer's current house and that includes abedroom on the main floor (the sizes and layouts of the homes beingdetermined using, e.g., third party computing devices 140 of suchsources as county property records and realtors) for more than 15minutes. These three inputs may be used to generate three fragmentedissue indicators 615 that, together, identify a financial goal of buyinga new house so that the mother can move in with. Advantageously, inputsrelated to one user's circumstances, goals, needs, etc., may be moreaccurately and/or quickly identified by acquiring and considering inputsfrom multiple user computing devices 200 associated with multiple otherusers (who may communicate about each other even if not directlyspeaking or otherwise communicating with each other). The fragmentedissue indicator 615 (as well as any of the other parameters in profile600) may also include an access permissions field that identifies whichfields (if any) of the fragmented issue indicator 615 (or otherparameter corresponding to the access field) are accessible toparticular advisors or other users.

In some implementations, a recommendation from the robo-advisor may bebased on one or more fragmented issue indicators 615. Additionally oralternatively, the provider computing device 110 and/or user computingdevice 120 may await a second (or third, fourth, etc.) signal that isrelevant to the first signal (or one or more prior signals if more thanone) and allows for a more informed or more targeted recommendation.Continuing with the above example, if the user computing device 120detects “I need to manage her finances,” application 250 may determinethere is a potential financial issue (based on, e.g., keywords orphrases such as “larger home” and “move in”) but may also determine thatmore information is desirable for formulating a suitable recommendation.Such information may, in some implementations, be acquired via dialoguewith the customer (e.g., an inquiry, conversation, or other informationexchange). For example, chatbot 270 of application 250 (running on,e.g., a consumer computing device 120) may speak with the customer toask general questions (e.g., inquiring whether the customer would likeassistance with a major purchase, followed by more specific questions)and/or specific questions (e.g., inquiring about the features of theitem to be purchased, such as style of car, number of bedrooms, etc.).

In certain implementations, when the second, third, or other signal isdetected, the provider computing device 110 and/or user computing device120 may access the fragmented issue indicators 615 for relatedinformation. Based on, for example, the first signal (e.g., related to aneed to look after the mother), application 250 may predict that thecurrent home of the customer is not large enough or does not have asuitable bedroom available and await one or more additional relatedsignals concerning, for example, the size or features of homes anddiscussions about open houses. In some implementations, location may betracked in response to a prediction that, for example, open houses orcar dealerships may be visited. The robo-advisor (via, e.g., providercomputing device 110 and/or user computing device 120) may then be moreinformed about the types of signals to look for (e.g., visiting openhouses or discussions about the adequacy of the current home), providemore informed recommendations, or ask more informed questions as part ofa dialogue with the customer. Alternatively or additionally, the secondsignal may be recorded as another fragmented issue indicator 615 forsubsequent use (e.g., in combination with a third signal detectedsubsequently).

In some implementations, the fragmented issue indicators 615 may be madeavailable to an advisor computing device 130 prior to or during a humanadvising session. Such fragmented issue indicators 615, or certainfields therein, may be recorded using, for example, “plain” text orother format that is readily interpretable by a financial advisor tohelp make the transition from robo-advisor to human advisor moreefficient by helping the advisor more quickly understand the customer'scircumstances (and consequent needs and goals). In some implementations,the user profile 600 may record encoded versions of the signals asfragmented issue indicators 615, and the decoding scheme may be madeaccessible to specified advisor computing devices 130 or other devicesto help control what information is shared (to save time that mightotherwise be spent reviewing information that is not particularlyrelevant to a topic to be discussed during an advising session, tobetter maintain confidentiality of certain information, etc.).

This approach assists with implementation of pervasive advising, as amore complete picture can be formed even though computing devices 200may only detect or acquire part of the picture (e.g., aspects of acustomer's circumstances) in a given time period. Multiple segments of adiscussion, user entries, etc., in multiple contexts, may be needed ordesired to enhance understanding of relevant financial issues and thusenhance the likely value and relevance of resulting recommendations.Advantageously, user computing devices 120 being used to detectconversations may not always detect a conversation in its entirety, oreven if a whole conversation is detected, not all of the words andmeanings may have been understood. For example, if the user computingdevice 120 detecting a conversation is a smartphone, and the smartphoneis placed in a pocket or bag during a conversation, the voices maybecome muffled, and the portion of the conversation during which thesmartphone is in the pocket or bag may be missed. Similarly, if the usercomputing device 120 is a smart speaker in one room, and one or morespeakers move out of the room or otherwise out of the range of the smartspeaker, portions of the conversation may be missed. By combiningfragmented issue indicators 615, a customer's needs can be evaluated andidentified over time as additional user inputs are detected.

Example profiles 600 may also include one or more fields related toexclusions and deferments 620. These fields may indicate, for example,that a customer does desire or need assistance with certain matters(exclusion of a matter), or may not desire or need assistance for acertain specified time period (deferment of matters). In someimplementations, application 250 may refer to exclusions and deferments620 before a recommendation is formulated or given. For example,conversationalists (via spoken words, written communications, etc.) maymake certain statements in certain contexts that are not, taken inisolation, valuable predictors of a user's goals or needs. For example,a speaker may make a statement with a friend for the purpose of making apoint, in jest, sarcastically, to be agreeable, and/or to sparefeelings. In a hypothetical, if a friend informs a customer that thefriend is traveling to a European city, and the customer states inresponse that the customer has wanted to visit that European city for along time, the customer does not necessarily need help with thefinancial goal of saving for a vacation. The customer may, for example,be thinking that he or she would like to visit the city years in thefuture, and/or has already established and is making progress towardsthe goal (as can be confirmed by application 250 accessing thecustomer's accounts, prior advising sessions, other communications,etc.), and consequently, the customer may not need to immediatelyaddress or revisit the issue. In some implementations, such a statementmay be deemed to warrant an entry in exclusions and deferments 620 ofthe customer's profile to help limit or avoid recommendations on certaintopics. Similarly, an exclusion and deferment 620 may be generated inresponse to a specific instruction or statement of a customer (e.g., acustomer stating to a consumer computing device 120 directly or making astatement to another person such as “I do not want to be advised on thistopic” or “that's not a priority of mine right now, I will deal withthat next month/year”). In some implementations, the information onparticular topics may still be saved to help form a better picture of acustomer's circumstances, but recommendations may be modified to avoidor delay certain topics.

Alternatively or additionally, certain statements may be analyzed togenerate entries in goals and progress 625 of profile 600. For example,continuing with the above example, the customer saying that he or shewants to visit the city in Europe may indicate that, for example, thecustomer likes to travel (if not already known or determined in anotherway) and/or that the customer may now be considering a vacation. Suchinformation, recorded in profile 600, may then be used by therobo-advisor, and/or presented to an advisor, to better informrecommendations and proposals.

Profile 600 may also include one or more session parameters 630.Application 250 (via, e.g., consumer computing device 120) may acceptsession parameters 630 (via, e.g., dashboard 510) to determine how ahuman advising session should be conducted. For example, a customer maywish to have audio only, text only, or video chat. The sessionparameters may be used by provider computing device 110, user computingdevice 120, and/or advisor computing device 130 to provide the customerwith human advising sessions that meet the customer's needs.

Additionally, a customer may only wish to receive automatedrecommendations in specified ways, something that can be indicated inrobo-advising parameters 635 of profile 600. In some implementations,the consumer computing device 120 may be programmed to only speak orotherwise make inquiries and provide recommendations under certainconditions but not under other conditions based on robo-advisingparameters 635. For example, if a user is speaking with a casual friend,it may not be appropriate to converse with the user to inquire as towhether the user wishes to pursue a specified (personal/confidential)financial goal (such as buying a new car) that is identified based onthe conversation with the casual friend. Rather, the user may wish toreceive recommendations when the user is alone, at home, with closefamily or friends only, during certain times and days (e.g., not duringwork hours, or not after dinner when the user may be winding down forsleep and not wishing to consider financial issues, or not on Sundays),and via certain channels and formats. In some implementations,robo-advising parameters 635 may, for example, prohibit a smart speakeror other consumer computing device 120 from disrupting the customer ordiscussing confidential topics at inappropriate times.

Profile 600 may also include human advising parameters 640. In someimplementations, human advising parameters 640 may indicate that acustomer wishes only to receive high-level advice on overall goals fromhuman advisors (e.g., to discuss the “big picture,” such as the prudenceand suitable timing for buying a new car). Similarly, the human advisingparameters 640 may indicate that the customer is additionally oralternatively interested in more specific advice on implementingparticular goals or executing on action plans (e.g., amounts to save,anticipated monthly payments, etc.). In certain implementations, thefields/values of human advising parameters 640 may be used by providercomputing device 110 and/or customer computing device 120 when matchinga customer with a suitable human advisor.

Profile 600 may additionally or alternatively include one or moreacquisition parameters 645. In one or more fields, acquisitionparameters 645 may specify how the customer is to be surveilled (e.g.,what inputs may be acquired, how various inputs are captured, etc.) andwhen/where the customer is not to be surveilled. In someimplementations, acquisition parameter 645 may indicate which consumercomputing devices 120 may be used to detect conversations. For example,a customer may wish to include/exclude detection of conversations viaidentified smartphones, smart speakers, smart watches, laptops, etc., tocontrol in what circumstances the customer's words may be taken intoconsideration (e.g., should or should not be used as a source of datafor advising purposes). Consumer computing devices 120 may be identifiedby, for example, device identification numbers and/or associated users.In various implementations, acquisition parameter 645 may, alternativelyor additionally, identify certain locations (as determined using, e.g.,location sensor 235) which are “off limits” and conversations should notbe surveilled. For example, a customer may identify a doctor's office asa location, and in response to detection that the consumer computingdevice 120 is located in, or has moved into, the identified location,the consumer computing device 120 may cease detection of conversationsfor use in advising the customer. This would allow the customer toexclude certain private conversations (with, e.g., a therapist) fromconsideration in advising. In some implementations, acquisitionparameters 645 may be used to indicate that conversations with certainpersons are included/excluded as advising inputs, and/or certain modesof communication are included/excluded as advising inputs. With suchacquisition parameters 645, a consumer computing device 120 may, forexample, not continue detecting a conversation in response toidentification of a specified speaker (by, e.g., recognizing a voicesignature, detecting the person's name used in a greeting, etc.), and/ormay include exclude certain electronic messages (e.g., text messagesand/or e-mails) received from specified applications and/orcommunication channels from being analyzed for inputs relevant toadvising of the customer.

Parameters and fields corresponding to profile 600 identified in FIG. 6help both the robo-advisor and the human advisor provide more relevantrecommendations in a personalized fashion, while more quickly focusingon the topics on which a customer wishes to receive assistance. Theyalso help customers more seamlessly transition between robo-advising andhuman advising, allowing the more efficient form of advising to be usedbased on customers' circumstances.

Referring to FIG. 7 , an example overview of a set of goals associatedwith a hypothetical customer is depicted. The first column indicateswhat major expenditure is desired/needed. In this example, the customerhas four goals, each goal having a different timeline depending on theneeds of the customer. For example, the customer may wish to travel toEurope within a year, buy a new car within 6 months, purchase a weddingring as soon as a ring is selected, and purchase a new home within twoyears. The second column indicates how much the customer could afford tospend and/or how much of a loan the customer would be approved for ifthe provider were to underwrite a loan for the expenditure now. Theaffordability amount and interest rate may be based at least in part onwhat the customer could afford based on his or her financialcircumstances.

In some implementations, the affordability fields may also include aprediction of how much could be afforded if the customer where to defermaking the purchase, or how much of a loan may be underwritten at sometime in the future (and, potentially, at what interest rate) if thecustomer were to wait for a certain time and stay on track withfinancial goals (e.g., budgets, savings, debt management, etc.). Theamount of time may be the same for each goal (e.g., 3 months, 6 months,1 year), may be different based on the type of goal (e.g., largerexpenditures might have a longer timeline), or may be customized by thecustomer and/or an advisor based on the customer's goals, financialcircumstances, and advisor (robo or human) recommendations. In theexample depicted in FIG. 7 , for each case, the customer could beapproved for a greater amount and at a better interest rate in thefuture. This may be based on various assumptions and on a requirementthat the customer takes certain actions. For example, the higher amountand/or lower interest rate may be based on such assumptions as: thecustomer having more funds saved; a track record of staying withindesignated budgets; rising value of assets (e.g., rise in value of ahome or investments); the customer continuing to make on-time andadditional (i.e., above minimum) payments for all debts and therebyenhancing his or her internal score (and/or external credit score); etc.The future anticipated amount may also be based on macroeconomicfactors, such as interest rates going up or down or staying about thesame as expected.

In the third column, FIG. 7 provides example recommendations for how toachieve a goal and/or when to make a purchase. These may be based onrobo advising (implemented using, e.g., provider computing device 110)and/or on human advising (received, e.g., via advisor computing device130). Such recommendations may be based on the financial circumstancesof the customer, on realistic steps that could be taken by the customerto improve his or her financial situation, and/or on external factorsthat are independent of the customer. For example, the providercomputing device 110 may access various third party computing devices140 to determine that fuel costs are coming down, which might beexpected to result in lower fares and impact what the customer couldafford. Similarly, the provider computing device 110 and/or consumercomputing device 120 may determine that car inventories are predicted togo up or down, that the cost of precious metals is changing, that homemortgage interest rates are changing, and/or that home values are goingup where the customer resides and/or where the customer wishes topurchase a home. In some implementations, the recommendations may, atleast in part, be based on human advising received via advisor computingdevice 130.

The provider computing device 110 and/or consumer computing device mayalso access (e.g., using various third-party APIs) various sources ofinformation to identify different suitable options for the type of majorexpenditure. For example, the provider computing device 110 may accessone or more third party computing devices 140 of various travel sites(such as Expedia, Travelocity, Kayak, etc.) to determine various flightand lodging options for travel, of dealer websites to determine variouscar options suited to the needs of the customer, of merchant websites todetermine various ring options, and/or of various realty sources todetermine home options. In various implementations, for each goal, theprovider computing system 110 and/or consumer computing device 120 mayretrieve data on various purchases via, for example, external resourcemodule 295. For example, for the goal of traveling to Europe, theprovider computing system 110 and/or consumer computing device 120 maydetermine that there are two flight options (Flight 1 and Flight 2 byaccessing API services provided by Travel Sites 1 and 2) that havedifferent costs (e.g., $800 and $620). The options presented may bedetermined to be suitable for the customer based on the needs anddesires of the customer as ascertained via application 250 running onthe consumer computing device 120. In some implementations, the optionsmay be provided even if they are expected to change by the time thecustomer is ready to make the purchase in order to keep the customermore informed (about, e.g., how much money he or she may need for themajor expenditure or what the customer could afford to purchase).

Such an overview (or a subset of the data represented therein) may beupdated in response to actions taken by the customer. In someimplementations, the data may be made accessible at any time via anapplication 250 running on a consumer computing device 120. The consumercomputing device 120 could be used, for example, to view changes to theapproval amounts and rates on, for example, a daily or hourly basis. Invarious implementations, the data is updated shortly after each relevantaction or change in circumstances detectable by the provider computingdevice 110 (e.g., opening accounts and/or making payments via theconsumer computing device 120). In some implementations, the approvalamount and interest rate may be updated based on factors independent ofthe customer (such as changes in interest rates, inclement weather,world events, etc.) which could be determined, for example, by theprovider computing device 110 by accessing a third party computingdevice 140.

It is noted that any of the icons or screen elements in the figures canbe structured to be clickable or otherwise selectable (using any inputmechanism, such as a touchscreen, mouse, voice prompt, gesture, etc.)for accessing additional information (such as details about a goal,underwriting, account, expenditure, available options, etc.), and/or formaking authorized changes (such as changing goal parameters, adding ormoving funds needed for an identified goal, etc.).

Referring to FIG. 8 , an example graphical user interface of, forexample, a potential dashboard 310 is illustrated. The user interface,which may be viewable via consumer computing device 120 and/or advisorcomputing device 130, simultaneously or at different times, providesinformation on financial goals. The issues may have been identified andrefined via robo-advising, human advising, or both. Also identified inthe example user interface are accounts held by, or otherwise accessibleto or viewable by, the customer. These accounts may be used in thefulfilment of financial goals, such as by having provider computingdevice 110 and/or customer computing device 120 transfer funds to/fromsuch accounts or control spending by using credit accounts withparticular limits, for certain expenses, etc.

The user interface may also identify advisors which whom the customerhas conferred. In various implementations (not shown in FIG. 8 ), theinterface may also identify, for example, the topics discussed with eachadvisor, the availability of each advisor, or the recommendations ofeach advisor. Also identified in FIG. 8 are the family members of thecustomer. If authorization is obtained from the family members, even ifthey are not customers or otherwise being separately advised,conversations or other inputs of the family members may be used tobetter understand the goals and needs of the customer and therebyenhance the quality of recommendations and transitions betweenrobo-advising and human advising. Some or all of the information indashboard 510 may be stored or identified in profile 600. For example,fragmented issue indicators 615 for all of the known family members maybe included in profile 600.

With reference to FIG. 9 , which depicts an example communicationbetween a consumer computing device 120 and a provider computing device110 or an advisor computing device 130, in some examples, a person(e.g., a customer) may have difficulty keeping track of his or herfinances and managing his or her credit. The person may be expecting togrow his or her family and wish to get his or her finances under controlin order to meet a financial goal of, for example, purchasing a newvehicle in anticipation of having a larger family. In some examples,based on recent transaction history indicating the possibility of a newbaby and/or a transaction such as a newly established college fund, theprovider computing device 110 may pervasively inquire, via a consumercomputing device 120 (e.g., a proactive listing bot), whether the personwould like some help with meeting a financial goal. The financial goalmay include buying a new car, traveling before a new baby arrives (e.g.,a “baby moon”), etc. The consumer device 120 may listen and interpretthe voice input of the person that indicates a desire to meet afinancial goal. In some examples, the provider computing device 110 maypervasively inquire, via a consumer computing device 120, whether theperson would like to set up a virtual meeting (e.g., a session or anappointment) with a banker to discuss the financial goals of the person.After the customer confirms that he or she is interested in a sessionwith an advisor, the provider computing device 110 may generate acommand structured to add the virtual meeting to a calendar accessibleto consumer computing device 120 associated with the customer and/or acalendar accessible to advisor computing device 130 of an advisor, asshown by the calendar and/or schedule icon (“April 15”) in FIG. 9 .

In some embodiments, the provider computing device 110 may be part ofthe computing system of a financial institution. Generally, thefinancial institution provides financial services (e.g., demand depositaccounts, credit accounts, etc.) to a plurality of customers. Thefinancial institution provides banking services to the customers, forexample, so that customers can deposit funds into accounts, withdrawfunds from accounts, transfer funds between accounts, view accountbalances, and the like via one or more provider computing devices 110.

FIG. 10 depicts an example graphical user interface of a potentialdashboard 510 structured to provide robo or human advising according toexample embodiments. The consumer computing device 120 may output, viathe graphical user interface, a user profile 1005 associated with theuser based on, for example, transaction or account data. The userprofile 1005 may identify the user and provide relevant informationpertaining to the user (e.g., user name “Nancy Isau,” customer status“Customer Since 2012,” etc.). In some examples, the graphical userinterface may include or otherwise display data and interactions (e.g.,conversations, transactions, and/or other relevant data) as representedby icons and/or graphics 1010 that have been compiled by the providercomputing device 110 and/or consumer computing device 120 for that user(e.g., the customer's photograph). This may allow a human advisor toseamlessly start the session with the user where the consumer computingdevice 120 and advisor computing device 130 ended a priorconversation/engagement. The dashboard 510 may also provide a “Return toRobo-Advising” selection 1015 to end the session and return the customerto robo-advising. In some implementations, this selection only becomesavailable when “back to bot” triggers are detected.

FIG. 11 depicts an example graphical user interface of a dashboard 510according to example embodiments. The provider computing device 110and/or consumer computing device 120 may be structured to generate anexpense strategy according to a time period (e.g., a timeline, one ormore minutes, hours, days, years, etc.). During the session (e.g., thevirtual robo-advising session with provider computing device 110 orhuman advising session with advisor computing device 130), the advisormay develop an expense strategy that may be implemented over a certainperiod of time based on one or more financial goals. The expensestrategy may include one or more icons and graphics structured torepresent, for example, a “5 Year Timeline” and/or financial goals ofthe user. In some arrangements, the graphical user interface may includean image and/or video of an advisor, or audio of the voice of anadvisor. The image, video, and/or the audio of the advisor may beprovided in real-time or near real-time such that the user may view orotherwise engage with the advisor live. In various implementations,multiple advisees and/or multiple advisors may interact live viadashboard 510. In some implementations, an advisor (e.g., “Advisor 2”)may be a robo-advisor helping one or more human advisors (e.g., “Advisor1”) advise or otherwise assist one or more users (e.g., Users 1 and 2).

FIG. 12 depicts an example graphical user interface 1200 of a majorexpenditure advising system 100 according to example embodiments. Insome implementations, the data is maintained at provider computingdevice 110. In various implementations, the graphical user interface ispart of a dashboard that is accessible via application 250 running onconsumer computing device 120. In certain implementations, the dashboardis also accessible via advisor computing device 130 if, for example, thecustomer computing device 120 and the advisor computing device 130 areengaged in an advising session (e.g., a video chat, audio chat, and/ortext chat). Such a graphical user interface may be used to present anexpense strategy 1210 customized for the customer (via, e.g., roboadvising and/or human advising) to help the customer achieve the goal ofmaking a major purchase (e.g., buying a new car). As depicted by icon1220, the consumer computing device 120 may speak, or output the speech,conversation, voice, etc. of the human (or robo) advisor during asession. For example, the advisor may suggest that the customer makemicro-payments on each credit card by setting up auto-pay (weekly,bi-weekly, monthly, etc.) for each credit card to increase the amount ofpayments that the user makes on time. In turn, the internal credit scoreof that customer may increase more quickly (as indicated by the +4, +6,and +7 pts corresponding with increases of 4, 6, and 7 points if one,two, or three monthly micropayments are made by the customer,respectively). In some examples, an expense strategy 610 may bedisplayed with a proposed change in spending, debt payoff,micropayments, etc. The expense strategy may be represented or furtherdetailed by one or more tabs 1230. For example, the consumer computingdevice 120 may be used to switch from the “Set Up Micropayments” to the“Curb Spending” or “Pay Off Debt” tabs. The tabs 1230 may be structuredto display the expense strategy details dynamically responsive to a userclicking or selecting a tab.

FIG. 13 depicts an example graphical user interface 1300 of a potentialdashboard 510 according to example embodiments. In some examples, theconsumer computing device 120 and/or the advisor computing device 130may output the graphical user interface 1300. The graphical userinterface 1300 may represent a digital dashboard with icons, images,data, charts, other graphics, etc., that may represent a financial goal,action plan, goal progress, etc. In some arrangements, the graphicaluser interface 1300 may include an image and/or video 1310representative of an advisor, or audio of the voice of an advisor(and/or a transcription of words spoken by the advisor). The image,video 1310, and/or the audio of the voice of the advisor may be providedin real-time or near real-time such that the user may view or otherwiseengage with the advisor live.

Illustrated in FIG. 14 are example expense strategy notifications (e.g.,messages, notices, account updates, invitations, offers, etc.). In someimplementations, the provider computing system 110 may provide, send, orotherwise transmit an expense strategy notification to consumercomputing device 120 associated with the customer based on detection ofa certain transaction or other action or event. The expense strategynotification may be output or otherwise displayed via a user interface200 (e.g., via a display, speaker, other audio/visual components of theconsumer computing device 120). The expense strategy notification mayindicate or otherwise inform the user of an action that affects thefinancial goal of the user as depicted in the graphical user interface1400. As shown in FIG. 14 , the expense strategy notification mayoutput/indicate when the user took an action that affects the expensestrategy. For example, the expense strategy notification may include atime stamp, date stamp (“May 3 Nancy paid . . . ,” “May 15 Nancy setspending limits,” etc.), or other indications of when an actionoccurred. The expense strategy notification may include a financial goalstatus based on the action or a plurality of actions taken by the userthat affect the expense strategy. In some examples, the providercomputing device 110 may transmit an expense strategy notification tothe consumer computing device 120 and/or advisor computing device 130 toinform the customer and/or advisor of the amount that the customer canafford to spend or save based on the financial situation of thecustomer. As shown, the expense strategy notification may includeactions from a single user or a plurality of users (e.g., “Bill saved$200 to New Car Goal”, “Nancy set up micro-payments,” etc.).Advantageously, users may find the expense strategy notification asmotivational and helpful to improve their financial status and to reachtheir financial goals (e.g., the goal to purchase a new car).

FIG. 15A depicts an example graphical user interface of an exampledashboard 510. The illustrated exchange may be between the customercomputing device 120 and the advisor computing device 130 (as part of ahuman advising session), and/or the exchange may be between the consumercomputing device 120 and the provider computing device 110 (as part of arobo-advising session). According to an example embodiment, the consumercomputing device 120 may present the expense strategy (e.g., advice,suggestions, etc.), which may have been, for example, generated by theprovider computing device 110 and/or updated via an advisor computingdevice 130, to the customer to help the customer review, maintain, orimprove progress towards a financial goal. The graphical user interfacemay be displayed such that the expense strategy may include iconsindicative of a goal status (e.g., checkmarks for completed oraccomplished, and exclamation points for incomplete or otherwiserequiring attention or response). In some implementations, iconspresented along a line may indicate an order or timeline for the goals(e.g., one goal may build on a preceding goal or may otherwise followanother goal temporally). The icons may correspond to one or morestrategies such as, but not limited to, spending limits, micropayments,car pre-payments, car purchase, etc. In some examples, the icons mayindicate whether the customer is off track or on track toward reachingthe financial goal. For example, icon 1505 indicates that the customeris off-track with maintaining spending limits toward the goal ofpurchasing a new car. In some examples, the graphical user interface mayallow the user to drill down to receive more detail. For example, acustomer may click (or otherwise select) icon 1505 and/or provide avoice command to see more information about how the customer may getback on track toward meeting the financial goal.

In some examples, if a customer selects one of the identified goals inFIG. 15A, another graphical user interface, such as the one depicted inFIG. 15B, may be presented. The graphical user interface may includeicons/graphics that represent, for example, the spending limits of acustomer. The graphical user interface may include an adjuster icon 1550(e.g., a graphical dial, slider control, etc.) structured to allow thecustomer (or advisor) to adjust/control, via dashboard 510, variousvalues (such as spending limits) as desired by the user. For example,the icon 1550 may be adjusted up, down, left, right, or in any otherdirection/position via the customer computing device 120 and/or advisorcomputing device 130. In some examples, the icon 1550 may represent aspending limit that is adjustable via the provider computing device 110(as part of robo-advising) or the advisor computing device 130 (as partof human advising). Responsive to the adjustment of the icon 1550, thespending limits of the user may then represent whether the user isoff-track or on-track toward reaching the financial goal. The providercomputing device 110, consumer computing device 120, and/or advisorcomputing device 130 may update profile 600 (e.g., by entering,updating, or revising values in fields corresponding to the goals andprogress 625 parameter). In some arrangements, the graphical userinterface may include an image and/or video representative of an advisor(e.g., at the top right in FIG. 15A) and/or audio of the voice of anadvisor in real-time or near real-time such that the user may view orotherwise engage with the advisor live. The graphical user interface mayinclude a selection (e.g., the virtual “Save Changes” button) to allowthe customer or advisor to save adjustments to the expense strategy,spending limits, etc.

FIG. 15C depicts an example graphical user interface of an potentialdashboard 510 according to example embodiments. In variousimplementations, the provider computing device 110 may present thegraphical user interface depicted in FIG. 15C to the customer via theconsumer computing device 120 and/or to the advisor via advisorcomputing device 130. In some examples, the graphical user interface maybe presented to the user in response to the user clicking an icon orbutton and/or providing a voice command as described herein. Thegraphical user interface may include a notification, message, and/orupdate that includes the current status of the user toward meeting afinancial goal. As depicted in FIG. 15C, the checkmark icon (formerly anexclamation point) adjacent to “Spending Limits” may indicate thecustomer is back on track based on, for example, the limits,transactions, adjustments made (via, e.g., the graphical user interfaceof FIG. 15B), or other actions of the customer.

FIG. 16 depicts an example graphical user interface of an potentialdashboard 510. According to an example embodiment, the providercomputing device 110 may present the graphical user interface to thecustomer and/or advisor via the consumer computing device 120 and/or theadvisor computing device 130. In some examples, the graphical userinterface may represent a digital dashboard that includes icons, images,data, charts (e.g., the graphical charts/graphs), other graphics, etc.,that may represent the credit score, spending trends, status of thecustomer toward reaching the financial goal, etc. According to thecurrent example depicted in FIG. 16 , the customer has made 100%progress toward the financial goal of buying a new car. The content ofthe digital dashboard may be provided in real-time or near real-time bythe provider computing device 110. Advantageously, the customer may beinformed of the current status of reaching the financial goal based onthe real-time or near real-time update of the digital dashboard 510.

FIG. 17 is an example graphical user interface of a potential dashboard510 according to an example embodiment. In some examples, the providercomputing device 110 is structured to generate an expense strategynotification (e.g., a message, SMS, notice, account update, invitation,offer, etc.). The provider computing device 110 may provide, send, orotherwise transmit the expense strategy notification to a consumercomputing device 120 and/or advisor computing device 130. The expensestrategy notification may be output or otherwise presented via adisplay, speaker, other audio/visual components of the consumercomputing device 120 and/or the advisor computing device 130. Forexample, the expense strategy notification may include an offer for anauto loan transmitted to the consumer computing device 120 of thecustomer when the customer meets the financial goal as identified by theprovider computing device 110 and/or advisor computing device 130. Insome implementations, an expense strategy notification that includes anoffer may be transmitted to the consumer computing device 120 inresponse to the consumer computing device 120 transmitting an expensestrategy notification (e.g., a SMS that includes information that thecustomer is ready to buy the car, a home, etc.) to the providercomputing device 110 and/or the advisor computing device 130.

As discussed above, a customer may be proximate to a locationcorresponding with a transaction point for a major purchase (e.g., acustomer may drive or walk by a car dealership). While at or near thelocation, the customer may think about a new car that he or she wants topurchase. As the customer passes the car dealership, the consumercomputing device 120 may determine that the customer is at or near thecar dealership. In some examples, while at or near the location, thecustomer may receive an expense strategy notification (e.g., ageo-located notification) from the provider computing device 110 thatthe customer is approved for a loan (e.g., a car loan). The providercomputing device 110 may recommend the expense strategy based on thetransactions, current budget, and spending patterns of the customer. Theexpense strategy may include a recommendation to the customer thatincludes the payment amount (e.g., a monthly payment amount) that thecustomer can safely afford.

The location monitor 265 may determine (e.g., via GPS coordinates and/orcellular network triangulation data acquired via location sensors 235)the location of the user associated with the consumer computing device120. In such examples, the consumer computing device 120 may beapproaching (e.g., as the customer is walking, driving, riding, runningtowards, etc.) or visiting the car dealership. In some implementations,the consumer computing device 120 may transmit location data to theprovider computing device 110.

While the consumer computing device 120 is at or near the cardealership, the provider computing system 110 may generate an expensestrategy (e.g., a financial plan, budget, investment strategy, etc.)and/or loan (e.g., an offer for a loan) structured to meet the financialgoal corresponding with the location of the consumer computing device120. As used herein, the term “loan” may be used to refer to a car loan,auto loan, personal loan, mortgage, student loan, line of credit, etc.The expense strategy and/or loan may be based on one or more parameterssuch as, but not limited to, the user account information, userfinancial information, current budget, and spending patterns. Theexpense strategy and/or loan may be displayed or otherwise communicatedto the user via an application 250 running on consumer computing device120. The application may receive an indication of acceptance of theexpense strategy and/or the loan when the customer clicks a link,button, icon, image, etc. via the consumer computing device 120.Alternatively or additionally, the consumer computing device 120 mayreceive an indication of acceptance in response to receiving a voiceinput (e.g., the user says a voice trigger or voice key, such as, butnot limited to, “accept loan,” “I accept the offer for the loan,” “Iaccept the expense strategy,” etc.).

Before pervasive advising is provided and/or an expense strategygenerated, a customer may be authenticated to the provider computingdevice 110 and/or consumer computing device 120. The customer may be anaccount holder already, or may open accounts or otherwise provide accessto various accounts that may be administered by the provider or by otherfinancial institutions. The customer may be authenticated based on theauthentication credentials of the customer. In some arrangements, thecustomer may be identified and authenticated based on an application 250that was made available via the provider computing device 110 (e.g., bybeing transmitted to an application store) for download by the consumercomputing device 120 such that additional identification information oraccount information from the customer is not required. The userauthentication data may include any of a password, a PIN (personalidentification number), a user ID, an answer to a verification question,a biometric, an identification of a security image, or a combinationthereof. The authentication circuitry 230 and/or application 250 maycompare the received authentication data with known and verified userauthentication data. If the authentication data provided does not matchthe known and verified user authentication data, the customer is notauthenticated. In some implementations, consumer computing device 120receives a voice input (e.g., a voice trigger, voice key, etc.)indicative of a financial goal while actively listening toconversations. For example, a user (e.g., a customer, potentialcustomer, other person, etc.) may be contemplating buying a new car. Thecustomer may make such statements as “I want to purchase a new car,” “Iwant to save for a home,” etc.

In some implementations, provider computing device 110 and/or consumercomputing device 120 may provide advice or otherwise make suggestions tothe customer and/or engage in conversation, discussion, or dialogue withthe user to learn more about the financial goal and to generate anexpense strategy that may be of interest to the user. In some examples,the consumer computing device 120 may be structured to ask the customerquestions or otherwise request feedback from the user, such as, “howmuch do you want to pay for the new car?”, “how much do you want themonthly loan payment to be?”, etc. The consumer computing device 120 maybe structured to detect the voices of multiple persons and distinguishthe voice input associated with the customer from the voice input orsound associated with another person or user. Alternatively oradditionally, the provider computing device 110 may learn that thecustomer is contemplating a financial goal (e.g., purchasing a new car)from a financial professional (e.g., a banker) who may be assisting theuser with financial planning (via, e.g., advisor computing device 130 orthrough other suitable channels).

In some implementations, there may be a time period between the receiptof the voice input and the generation of an expense strategy such thattransaction data, the voice input, etc. may be stored for later useand/or retrieval. For example, the customer may have expressed aninterest in the financial goal (e.g., the purchase of a new car, home,property, etc.) minutes, hours, days, or months ago such that the voiceinput, transaction data, etc. may be stored in memory or any othersuitable storage. Later, the voice input, transaction data, etc., may beretrieved or otherwise accessed for generation of an expense strategyand/or loan offer as described herein. For example, the customer mayhave expressed an interest in purchasing a new car several months agowhen the desire was not urgent or otherwise was not a priority. When theconsumer computing device 120 listens to the conversation of the userand detects that the customer is now expecting to have a baby, the voiceinput, transaction data, etc., may be retrieved or otherwise accessedfrom storage.

As described herein, the loan underwriting may be generated or otherwisetriggered based on the location (e.g., the geolocation) of the user. Inexamples wherein the user is not ready to accept the expense strategyand/or the loan, there may not be an impact on the credit score of theuser. There may not be an impact on the credit score of the user where,for example, the underwriting of the loan occurred based on userfinancial information maintained using one or more provider computingdevices 110 (e.g., the internal financial institution data). Forexample, though the user is at a car dealership looking at cars, theuser may not be ready to purchase a new car. Because the providercomputing device 110 may have generated the loan or otherwisepre-qualified the user for the loan based on the internal financialinstitution credit score and/or financial patterns with financialinstitution, there is not an impact on the credit score of the user.Advantageously, the user may receive an expense strategy and/or an offerfor a loan without an impact to their credit score associated with anexternal credit bureau or other financial institution.

In some examples, the provider computing device 110 may be structured toadjust the approval amount of the loan based on one or more parameterssuch as, but not limited to, the customer account information, financialinformation, current budget, and spending patterns. The approval amountmay be adjusted in real-time or near real-time. The financialinformation of the customer may be indicative of a change in thefinances of the user. For example, the internal financial score of theuser may have improved such that the underwriting engine 255 maygenerate a loan at a lower interest rate than the previous loangenerated. In such examples, the approval amount for which the user hasbeen approved may be adjusted (e.g., dynamically updated) in response toa transaction performed or executed by the user. For example, theanalytics engine 275 may analyze the financial information of the userand/or the spending patterns of the user to generate transaction data.The transaction data may be provided to or retrieved by the underwritingengine 255 for use in determining loan amounts and/or interest rates.

In some implementations, if the customer took an action (e.g., paid thebalance of a credit card off, made a payment on time, etc.) that had afavorable or unfavorable result on the financial information (e.g., thecredit score) of the user, the user may receive an expense strategynotification that includes such feedback (e.g., favorable feedback) forhaving taken that action. For example, an expense strategy notificationmay include a message like or similar to, “with your new lower interestrate due to your improved credit score, you can now afford a car thatcosts 27,500!”

The embodiments described herein have been described with reference todrawings. The drawings illustrate certain details of specificembodiments that implement the systems, methods and programs describedherein. However, describing the embodiments with drawings should not beconstrued as imposing on the disclosure any limitations that may bepresent in the drawings.

It should be understood that no claim element herein is to be construedunder the provisions of 35 U.S.C. § 112(f), unless the element isexpressly recited using the phrase “means for.”

The various components of the computing systems and user devices (suchas modules, monitors, engines, trackers, locators, circuitry,interfaces, sensors, etc.) may be implemented using any combination ofhardware and software structured to execute the functions describedherein. In some embodiments, each respective component may includemachine-readable media for configuring the hardware to execute thefunctions described herein. The component may be embodied at least inpart as one or more circuitry components including, but not limited to,processing circuitry, network interfaces, peripheral devices, inputdevices, output devices, sensors, etc. In some embodiments, a componentmay take the form of one or more analog circuits, electronic circuits(e.g., integrated circuits (IC), discrete circuits, system on a chip(SOCs) circuits, etc.), telecommunication circuits, hybrid circuits, andany other type of circuit. In this regard, the component may include anytype of element for accomplishing or facilitating achievement of theoperations described herein. For example, a component as describedherein may include one or more transistors, logic gates (e.g., NAND,AND, NOR, OR, XOR, NOT, XNOR, etc.), resistors, multiplexers, registers,capacitors, inductors, diodes, wiring, and so on).

The component may also include one or more processors communicativelycoupled to one or more memory or memory devices. In this regard, the oneor more processors may execute instructions stored in the memory or mayexecute instructions otherwise accessible to the one or more processors.In some embodiments, the one or more processors may be embodied invarious ways. The one or more processors may be constructed in a mannersufficient to perform at least the operations described herein. In someembodiments, the one or more processors may be shared by multiplecircuits (e.g., circuit A and circuit B may comprise or otherwise sharethe same processor which, in some example embodiments, may executeinstructions stored, or otherwise accessed, via different areas ofmemory). Alternatively or additionally, the one or more processors maybe structured to perform or otherwise execute certain operationsindependent of one or more co-processors. In other example embodiments,two or more processors may be coupled via a bus to enable independent,parallel, pipelined, or multi-threaded instruction execution. Eachprocessor may be implemented as one or more general-purpose processors,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), digital signal processors (DSPs), or other suitableelectronic data processing components structured to execute instructionsprovided by memory. The one or more processors may take the form of asingle core processor, multi-core processor (e.g., a dual coreprocessor, triple core processor, quad core processor, etc.),microprocessor, etc. In some embodiments, the one or more processors maybe external to the apparatus, for example the one or more processors maybe a remote processor (e.g., a cloud based processor). Alternatively oradditionally, the one or more processors may be internal and/or local tothe apparatus. In this regard, a given components or parts thereof maybe disposed locally (e.g., as part of a local server, a local computingsystem, etc.) or remotely (e.g., as part of a remote server such as acloud based server). To that end, a component as described herein mayinclude elements that are distributed across one or more locations.

An example system for implementing the overall system or portions of theembodiments might include a general purpose computing computers in theform of computers, including a processing unit, a system memory, and asystem bus that couples various system components including the systemmemory to the processing unit. Each memory device may includenon-transient volatile storage media, non-volatile storage media,non-transitory storage media (e.g., one or more volatile and/ornon-volatile memories), etc. In some embodiments, the non-volatile mediamay take the form of ROM, flash memory (e.g., flash memory such as NAND,3D NAND, NOR, 3D NOR, etc.), EEPROM, MRAM, magnetic storage, hard discs,optical discs, etc. In other embodiments, the volatile storage media maytake the form of RAM, TRAM, ZRAM, etc. Combinations of the above arealso included within the scope of machine-readable media. In thisregard, machine-executable instructions comprise, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions. Each respective memory devicemay be operable to maintain or otherwise store information relating tothe operations performed by one or more associated circuits, includingprocessor instructions and related data (e.g., database components,object code components, script components, etc.), in accordance with theexample embodiments described herein.

Any foregoing references to currency or funds are intended to includefiat currencies, non-fiat currencies (e.g., precious metals), andmath-based currencies (often referred to as cryptocurrencies). Examplesof math-based currencies include Bitcoin, Litecoin, Dogecoin, and thelike.

Any foregoing references to currency or funds are intended to includefiat currencies, non-fiat currencies (e.g., precious metals), andmath-based currencies (often referred to as cryptocurrencies). Examplesof math-based currencies include Bitcoin, Litecoin, Dogecoin, and thelike.

It should be noted that although the diagrams herein may show a specificorder and composition of method steps, it is understood that the orderof these steps may differ from what is depicted. For example, two ormore steps may be performed concurrently or with partial concurrence.Also, some method steps that are performed as discrete steps may becombined, steps being performed as a combined step may be separated intodiscrete steps, the sequence of certain processes may be reversed orotherwise varied, and the nature or number of discrete processes may bealtered or varied. The order or sequence of any element or apparatus maybe varied or substituted according to alternative embodiments.Accordingly, all such modifications are intended to be included withinthe scope of the present disclosure as defined in the appended claims.Such variations will depend on the machine-readable media and hardwaresystems chosen and on designer choice. It is understood that all suchvariations are within the scope of the disclosure. Likewise, softwareand web implementations of the present disclosure could be accomplishedwith standard programming techniques with rule based logic and otherlogic to accomplish the various database searching steps, correlationsteps, comparison steps and decision steps.

The foregoing description of embodiments has been presented for purposesof illustration and description. It is not intended to be exhaustive orto limit the disclosure to the precise form disclosed, and modificationsand variations are possible in light of the above teachings or may beacquired from this disclosure. The embodiments were chosen and describedin order to explain the principals of the disclosure and its practicalapplication to enable one skilled in the art to utilize the variousembodiments and with various modifications as are suited to theparticular use contemplated. Other substitutions, modifications, changesand omissions may be made in the design, operating conditions andarrangement of the embodiments without departing from the scope of thepresent disclosure as expressed in the appended claims.

What is claimed is:
 1. A service provider computing system comprising: adatabase with a user profile corresponding to a user, the user profilecomprising a digital biometric voice signature of the user; and anetwork interface configured to communicatively couple the serviceprovider computing system with a mobile device having a sound sensor, alocation sensor, and one or more user interfaces, the mobile devicebeing configured to use the sound sensor to detect ambient sounds, usethe location sensor to detect physical locations of the mobile device,and use the one or more user interfaces to perceptibly presentinformation to the user; wherein at least one of the mobile device orthe service provider computing system is configured to: detect, usingthe sound sensor of the mobile device, a set of ambient soundscomprising one or more conversations between the user and one or moreother persons; analyze the set of ambient sounds to identify, based onthe digital biometric voice signature of the user, one or more phrasesspoken by the user during the one or more conversations; determine,based on the one or more phrases spoken by the user, an expense strategyfor an expenditure; detect, using a location sensor of the mobiledevice, a physical location of the mobile device; and audibly orvisually present the expense strategy to the user via the one or moreuser interfaces of the mobile device when the physical location is apredetermined physical location, and continue to present the expensestrategy only while the user remains within a predetermined radius ofthe physical location.
 2. The system of claim 1, wherein the digitalbiometric voice signature of the user is used to distinguish between theuser and the one or more other persons.
 3. The system of claim 1,wherein determining the expense strategy comprises identifying theexpenditure based on the one or more phrases spoken by the user.
 4. Thesystem of claim 1, wherein determining the expense strategy comprisesformulating the expense strategy for the expenditure based on theexpenditure and user data in the user profile.
 5. The system of claim 1,wherein determining the expense strategy comprises generating a loan tobe used for the expenditure, and wherein presenting the expense strategycomprises presenting the loan as an option for paying for theexpenditure.
 6. The system of claim 5, wherein the predeterminedphysical location is a merchant for the expenditure, and wherein theexpense strategy is presented upon determining that the detectedphysical location corresponds to the merchant.
 7. The system of claim 5,wherein at least one of the service provider computing system and themobile device is further configured to present the expense strategy inreal-time or near real-time such that initially-presented loan data isupdated based on subsequent transactions detected by at least one of themobile device and the service provider computing system.
 8. The systemof claim 1, wherein analyzing the set of ambient sounds comprisesidentifying a plurality of fragmented issue indicators in the one ormore conversations, wherein the expenditure is identified based at leastin part on a combination of fragmented issue indicators.
 9. The systemof claim 8, wherein the plurality of fragmented issue indicators arephrases spoken by the user during a plurality of conversations with aplurality of different persons on a plurality of different days.
 10. Thesystem of claim 1, wherein at least one of the mobile device and theservice provider computing system is further configured to determine asecond physical location of the mobile device based on a transactionexecuted via a mobile application running on the mobile device.
 11. Thesystem of claim 1, wherein at least one of the mobile device and theservice provider computing system is further configured to formulate theexpense strategy based at least in part on the physical location of themobile device.
 12. The system of claim 1, wherein at least one of themobile device and the service provider computing system is furtherconfigured to identify the expenditure based at least in part on thephysical location of the mobile device.
 13. The system of claim 12,wherein at least one of the mobile device and the service providercomputing system is further configured to identify the expenditure basedat least in part on proximity of the mobile device to a merchant at thephysical location.
 14. The system of claim 1, wherein the mobile deviceis a first mobile device, wherein at least one of the first mobiledevice and the service provider computing system is communicativelycoupled to a second mobile device, wherein the second mobile devicecomprises a second location sensor configured to determine a secondphysical location of the second mobile device, wherein at least one ofthe service provider computing system, the first mobile device, and thesecond mobile device is configured to identify the expenditure based atleast in part on the second physical location of the second mobiledevice determined using the second location sensor of the second mobiledevice.
 15. The system of claim 1, wherein each detected conversationcomprises first voice inputs of the user and second voice inputs of theone or more other persons, the first voice inputs corresponding to theuser speaking to the one or more other persons and the second voiceinputs corresponding to the one or more other persons speaking to theuser, and wherein at least one of the service provider computing systemand the mobile device is further configured to extract, from a subset ofthe ambient sounds, using the digital biometric voice signature of theuser to distinguish between the first voice inputs and the second voiceinputs, a subset of the first voice inputs comprising the plurality ofphrases spoken by the user to the one or more other persons.
 16. Acomputer-implemented method comprising: detecting, using a sound sensorof a user computing device, a set of ambient sounds comprising one ormore conversations between a user and one or more other persons;analyzing, by the user computing device or a service provider computingdevice in communication with the user computing device, the set ofambient sounds to identify, based on the digital biometric voicesignature of the user, one or more phrases spoken by the user during theone or more conversations; determining, by the user computing device orthe service provider computing device, based on the one or more phrasesspoken by the user, an expense strategy for an expenditure; detecting,using a location sensor of the user computing device, a physicallocation of the user computing device; and audibly or visuallypresenting, by the user computing device, the expense strategy to theuser via one or more user interfaces of the user computing device whenthe physical location is a predetermined physical location, andcontinuing to present the expense strategy only while the user remainswithin a predetermined radius of the physical location.
 17. Thecomputer-implemented method of claim 16, wherein determining the expensestrategy comprises formulating, by the user computing device or theservice provider computing device, the expense strategy based on theexpenditure and a user profile with user data corresponding to the user.18. The computer-implemented method of claim 16, wherein determining theexpense strategy comprises generating a loan to be used for theexpenditure, and wherein presenting the expense strategy comprisespresenting the loan as an option for paying for the expenditure.
 19. Thecomputer-implemented method of claim 16, wherein the predeterminedphysical location is a merchant for the expenditure, and wherein theexpense strategy is presented upon determining that the detectedphysical location corresponds to the merchant.
 20. A computing devicecomprising: a network interface configured to communicate with a serviceprovider computing system via a telecommunications network; a soundsensor configured to detect ambient sounds; a location sensor configuredto detect a physical location of the computing device; one or more userinterfaces for audibly or visually presenting information; and aprocessor and memory having instructions that, when executed by theprocessor, cause the processor to: detect, using the sound sensor, soundsamples comprising one or more conversations between a user of thecomputing device and one or more other persons; analyze the soundsamples to identify, using a digital biometric voice signature of theuser of the computing device to distinguish between first voice inputsof the user and second voice inputs of one or more other persons, anitem to be purchased by the user; receive, via the network interface,loan data from the service provider computing system for a loan optiongenerated for purchasing the item, the loan option being generated bythe service provider computing system based at least in part on the itemand a user profile corresponding to the user of the computing device;detect, using the location sensor, a first physical location of thecomputing device; determine that the first physical location is within apredetermined radius from a merchant that sells the item; in response todetermining that the first physical location is within the predeterminedradius from the merchant that sells the item, audibly or visuallypresent the loan option to the user via the one or more user interfaces;detect, using the location sensor, a second physical location of thecomputing device; determine that the second physical location is nolonger within the predetermined radius from the merchant that sells theitem; and in response to determining that the second physical locationis no longer within the predetermined radius from the merchant thatsells the item, cease presenting the loan option to the user via the oneor more user interfaces such that the loan option is presented onlywhile the user remains within the predetermined radius of the merchant.